---------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  D:\Dropbox\Xiangjun\FinalFiles\Replication\Results\All_in_One.log
  log type:  text
 opened on:  13 Oct 2022, 10:59:53

. 
. ********************************************************************************
. 
. ******** Table 1: Summary Statistics 
. 
. 
. do Programs\Table_1.do

. 
. ***************************************************************************************
. ************************** Table 1: Summary Statistics 
. ****************************************************************************************
. 
. ************************** PanelA: Hunan
. 
. use Data\HunanCntyYr.dta,clear

. 
. ********************************************************************************
. 
. 
. sum martyr martyrs_tot_hn Zeng_all0_invdist  Zeng_all0_invdist_pc lnarea lnpop   lnrice lnwheat mainriv  dist
> 2canal lnurbanpop capital  lnjinshi  lnquotas dist_nanjing route1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      martyr |      1,125    26.21333     145.748          0       3740
martyrs_to~n |      1,125    370.4267    1204.796          0       9497
Zeng_all0_~t |      1,125    1.231111    2.533128          0   15.83333
Zeng_al~t_pc |      1,125     4.45793    7.525672          0   43.38018
      lnarea |      1,125    7.841084    .4791726   6.554833   8.895059
-------------+---------------------------------------------------------
       lnpop |      1,125     12.1428    .6204288   10.48988   13.36323
      lnrice |      1,125    1.634289    .0983454   1.406914    1.94591
     lnwheat |      1,125    1.610524    .0615782    1.46228   1.800976
     mainriv |      1,125          .4    .4901158          0          1
  dist2canal |      1,125    8.738826    1.216765   6.439329   11.28337
-------------+---------------------------------------------------------
  lnurbanpop |      1,125    8.534782    1.482537   6.216606   12.45294
     capital |      1,125         .28    .5051338          0          2
    lnjinshi |      1,125    1.113293    1.068969          0     3.7612
    lnquotas |      1,125    2.625897    .3578146   1.791759   3.135494
dist_nanjing |      1,125    8.643134    1.262205   5.820728    11.0155
-------------+---------------------------------------------------------
      route1 |      1,125         .12    .3251061          0          1

. 
. 
. ********************************************************************************
. ********************************************************************************
. 
. ************************** PanelB: All counties
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. 
. sum  martyrs_tot_post Zeng_all0_invdist  alloff  lncntyarea lncntypop  lnrice lnwheat mainriv dist2canal   ln
> urbanpop  prefcap       lnjinshi  lncntyquota0   dist_nanjing   Taiping_route1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
martyrs_to~t |    182,706    .0168785    .2684177          0      9.497
Zeng_all0_~t |    182,706    .6773998    2.018581          0   44.83333
      alloff |    182,706    .0906319    .5382917          0         25
  lncntyarea |    182,706    7.397333    .8883175  -5.792674   11.46648
   lncntypop |    182,706    12.08437    1.023974  -2.231627   14.81712
-------------+---------------------------------------------------------
      lnrice |    182,706    1.343677    .3203666          0   2.014903
     lnwheat |    182,706    1.563785    .2998109          0   2.091864
     mainriv |    182,706    .4246659    .4942935          0          1
  dist2canal |    182,706    7.059183    5.564342   .0055672   21.92418
  lnurbanpop |    182,706    7.696716    2.763618          0   13.15386
-------------+---------------------------------------------------------
     prefcap |    182,706    .1427704    .3498395          0          1
    lnjinshi |    182,706    1.464476    1.228712          0   6.204558
lncntyquota0 |    182,706     2.50434    .8438704  -11.71261   4.279931
dist_nanjing |    182,706    9.122696    4.737305   .1616544   23.63911
Taiping_ro~1 |    182,706     .036452    .1874125          0          1

. 
.    
.         
.                 
. 
end of do-file

. 
. 
. ******** Table A3: Elite Connections and Other Characteristics cross Counties
. 
. 
. do Programs\Appendix_Table_A3.do

. 
. ***************************************************************************************
. ************************** Table A3： Elite Connections and Other Characteristics cross Counties
. ************************** Sample: Hunan counties, 1850--1864
. ****************************************************************************************
. 
. use Data\NationalCntyYr.dta,clear

. 
. *********************************************************************************
. 
. 
. reg     Zeng_all0_invdist   lncntyarea lncntypop    lnrice lnwheat mainriv dist2canal   lnurbanpop  prefcap  
>      lnjinshi  lncntyquota0   dist_nanjing   Taiping_route1 if year==1855&hunan==1,  cluster(  samcntyid)  

Linear regression                               Number of obs     =         75
                                                F(12, 74)         =       2.72
                                                Prob > F          =     0.0042
                                                R-squared         =     0.4204
                                                Root MSE          =     2.1201

                               (Std. Err. adjusted for 75 clusters in samcntyid)
--------------------------------------------------------------------------------
               |               Robust
Zeng_all0_in~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    lncntyarea |   2.530003   1.367367     1.85   0.068    -.1945345     5.25454
     lncntypop |   .5561944   .7781332     0.71   0.477    -.9942698    2.106659
        lnrice |  -1.713023   1.937265    -0.88   0.379    -5.573108    2.147063
       lnwheat |   .0588011   2.982192     0.02   0.984    -5.883346    6.000948
       mainriv |   .2549476   .6367115     0.40   0.690    -1.013728    1.523623
    dist2canal |   .5719599   .8700422     0.66   0.513    -1.161637    2.305557
    lnurbanpop |  -.1685393   .3684816    -0.46   0.649    -.9027549    .5656764
       prefcap |   .3791892   .7774691     0.49   0.627    -1.169952     1.92833
      lnjinshi |   1.274652   .4900419     2.60   0.011     .2982222    2.251082
  lncntyquota0 |  -3.119733   1.476555    -2.11   0.038    -6.061833   -.1776335
  dist_nanjing |  -.3621765   .9270886    -0.39   0.697    -2.209441    1.485088
Taiping_route1 |   1.516658    .779063     1.95   0.055    -.0356594    3.068975
         _cons |  -16.73753   9.735796    -1.72   0.090    -36.13653    2.661468
--------------------------------------------------------------------------------

. outreg2 using Results\Appendix_Table_A3.doc,  keep(lncntyarea lncntypop    lnrice lnwheat mainriv dist2canal 
>   lnurbanpop  prefcap       lnjinshi  lncntyquota0   dist_nanjing   Taiping_route1 ) sortvar(lncntyarea lncnt
> ypop     lnrice lnwheat mainriv dist2canal   lnurbanpop  prefcap       lnjinshi  lncntyquota0   dist_nanjing 
>   Taiping_route1) se  bdec(3) rdec(3) nocons  replace
Results\Appendix_Table_A3.doc
dir : seeout

. 
. 
. reg     Zeng_all0_invdist   lncntyarea lncntypop    lnrice lnwheat mainriv dist2canal   lnurbanpop  prefcap  
>      lnjinshi  lncntyquota0   dist_nanjing   Taiping_route1 if year==1855,  cluster(samcntyid)  

Linear regression                               Number of obs     =      1,646
                                                F(12, 1645)       =      19.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1409
                                                Root MSE          =     1.8784

                            (Std. Err. adjusted for 1,646 clusters in samcntyid)
--------------------------------------------------------------------------------
               |               Robust
Zeng_all0_in~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    lncntyarea |  -.0333198   .0811725    -0.41   0.682    -.1925322    .1258926
     lncntypop |   .3439391     .08722     3.94   0.000     .1728652    .5150129
        lnrice |   .3127543   .1939605     1.61   0.107    -.0676813    .6931898
       lnwheat |  -.3270048   .2229614    -1.47   0.143    -.7643228    .1103133
       mainriv |   .1211927   .1183062     1.02   0.306     -.110854    .3532393
    dist2canal |  -.0042844   .0409804    -0.10   0.917    -.0846637    .0760949
    lnurbanpop |  -.0834107   .0396766    -2.10   0.036    -.1612327   -.0055887
       prefcap |   .4478789   .1801542     2.49   0.013     .0945233    .8012346
      lnjinshi |   .5560647   .1096312     5.07   0.000     .3410333    .7710961
  lncntyquota0 |  -.2016496   .0972156    -2.07   0.038    -.3923289   -.0109703
  dist_nanjing |   .0217516   .0535374     0.41   0.685     -.083257    .1267603
Taiping_route1 |   .8597459   .3464024     2.48   0.013     .1803097    1.539182
         _cons |  -3.123581   .8044315    -3.88   0.000    -4.701398   -1.545763
--------------------------------------------------------------------------------

. outreg2 using Results\Appendix_Table_A3.doc,  keep(lncntyarea lncntypop   lnrice lnwheat mainriv dist2canal  
>  lnurbanpop  prefcap       lnjinshi  lncntyquota0   dist_nanjing   Taiping_route1 ) sortvar(lncntyarea lncnty
> pop     lnrice lnwheat mainriv dist2canal   lnurbanpop  prefcap       lnjinshi  lncntyquota0   dist_nanjing  
>  Taiping_route1) se  bdec(3) rdec(3) nocons   append
Results\Appendix_Table_A3.doc
dir : seeout

. 
. 
. 
end of do-file

. 
. 
. ******** Figure 3. Motivational Evidence on Elite Networks and Soldier Deaths: Raw Data
. 
. 
. do Programs\Figure_3.do

. 
. ***************************************************************************************
. ************************** Figure 3: Motivational Evidence on Elite Networks and Soldier Deaths
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. *********************************************************************************
. 
. gen connect=(Zeng_all0_invdist>0)

. 
.  
. **** **** **** 
. gen year0=year

. tabout year0 connect using Results\Figure_3.txt, replace  cells(mean martyr) format(3)  sum

Table output written to: Results\Figure_3.txt

        connect         
year0   0       1       Total
        Mean martyr     Mean martyr     Mean martyr
1850    0.590   1.361   0.960
1851    5.026   2.111   3.627
1852    9.769   6.472   8.187
1853    11.051  8.861   10.000
1854    16.744  63.528  39.200
1855    34.103  79.694  55.987
1856    4.077   46.056  24.227
1857    2.410   55.194  27.747
1858    1.769   113.750 55.520
1859    51.487  57.833  54.533
1860    7.385   37.972  22.067
1861    13.462  40.417  26.400
1862    4.744   39.472  21.413
1863    2.974   53.111  27.040
1864    3.872   29.750  16.293
Total   11.297  42.372  26.213

. 
. 
. **** **** **** **** **** **** ******* **** **** **** **** **** ******* **** **** **** **** **** ***
. **** **** **** **** **** **** **** graphing: parallel
.  
. clear

. import delimited "Results\Figure_3.txt"
(4 vars, 19 obs)

. drop in 1/3
(3 observations deleted)

. drop in 16
(1 observation deleted)

. destring v1, force gen(year)
v1: all characters numeric; year generated as int

. destring v2, force gen(NonConnect_martyr)
v2: all characters numeric; NonConnect_martyr generated as double

. destring v3, force gen(Connect_martyr)
v3: all characters numeric; Connect_martyr generated as double

. destring v4, force gen(tot_martyr)
v4: all characters numeric; tot_martyr generated as double

. 
. drop v1 v2 v3 v4 

. sum

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        year |         15        1857    4.472136       1850       1864
NonConnect~r |         15    11.29753    13.96579        .59     51.487
Connect_ma~r |         15    42.37213     30.9068      1.361     113.75
  tot_martyr |         15     26.2134    18.12135        .96     55.987

. sort year

. 
. twoway  connect  Connect_martyr year,  msymbol(O) mc(gs6)  lp(solid) lc(gs6) lw(medthick)  || ///
>  connect  NonConnect_martyr year,  legend(order(1 "Connected" 2 "Unconnected") row(2))    ylabel(0(40)120) ms
> ymbol(Oh) mc(gs6)   lp(dash) lc(gs6) lw(medthick)     ///
> xsize(8) ysize(6) xline(1853, lc(blue) lp(solid)) graphregion( color(white) ifcolor(white) ilcolor(white) fco
> lor(white))  title("Number of soldier deaths:" "connected & unconnected counties in Hunan", size(median) )  
(note:  named style median not found in class gsize, default attributes used)

. 
.   
. graph export Results\Figure_3.png, replace
(file Results\Figure_3.png written in PNG format)

.  
.  
. 
end of do-file

. 
. 
. ******** Figure A.5. II. Number of National-level Offices and Officials Over Time
. 
. 
. do Programs\Appendix_Figure_A5.do

. ********************************************************************************
. ******** Figure A.5. II. Number of National-level Offices and Officials Over Time
. ********************************************************************************
. 
. use Data\OfficialYear.dta,clear

. 
. ********************************************************************************
. 
. use Data\OfficialYear.dta,clear

. 
. tsset year
        time variable:  year, 1800 to 1910
                delta:  1 unit

. 
. keep if year>=1820
(20 observations deleted)

. foreach x of varlist tot_ind tot_han_ind tot_senior tot_han_senior {
  2. gen ma5_`x'=(`x'+l.`x'+l2.`x'+l3.`x'+l4.`x')/5
  3. }
(4 missing values generated)
(4 missing values generated)
(4 missing values generated)
(4 missing values generated)

. 
. 
. 
. twoway (scatter tot_senior year, m(O) msize(small) mc(gs6) xlabel(1820(30)1910)) ///
> (line ma5_tot_senior year, lp(solid) lc(gs2) lw(medium ))  ///
> (scatter tot_han_senior year, m(Oh) msize(small) mc(gs6)) ///
> (line ma5_tot_han_senior year, lp(dash) lc(gs2) lw(medium ) graphregion(color(white) ifcolor(white) ilcolor(w
> hite) fcolor(white)) xtitle(Year) legend(order(1 "Total number" 2 "5-year moving average"    3 "The Han Chine
> se" 4 "5-year moving average" ) row(2)) title(A: Number of positions) saving(Results\AllPositions_by_Year, re
> place)) 
(file Results\AllPositions_by_Year.gph saved)

. 
. twoway (scatter tot_ind year, m(O) msize(small) mc(gs6) xlabel(1820(30)1910)) ///
> (line ma5_tot_ind year, lp(solid) lc(gs2) lw(medium ))  ///
> (scatter tot_han_ind year, m(Oh) msize(small) mc(gs6)) ///
> (line ma5_tot_han_ind year, lp(dash) lc(gs2) lw(medium ) graphregion(color(white) ifcolor(white) ilcolor(whit
> e) fcolor(white)) xtitle(Year) legend(order(1 "Total number" 2 "5-year moving average"    3 "The Han Chinese"
>  4 "5-year moving average" ) row(2))  title(B: Number of officials) saving(Results\AllOfficials_by_Year, repl
> ace)) 
(file Results\AllOfficials_by_Year.gph saved)

. 
. 
. graph combine Results\AllPositions_by_Year.gph Results\AllOfficials_by_Year.gph, xsize(9) ysize(4.5) graphreg
> ion( color(white) ifcolor(white) ilcolor(white) fcolor(white))

. graph export Results\Figure_A5.png, replace 
(file Results\Figure_A5.png written in PNG format)

. 
. 
. 
end of do-file

. 
. 
. ******** Table 2. The Impact of Elite Connections on Soldier Deaths: DD Estimates
. 
. 
. do Programs\Table_2.do

. 
. ***************************************************************************************
. ************************** Table 2： The Impact of Elite Connections on Soldier Deaths: DD Estimates 
. ************************** Sample: Hunan counties, 1850--1864
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************************************************************************
. ********************** regression
. 
. 
. xi: reghdfe  lnmartyr1  Zeng_all0_invdist_Post , absorb(year cntyid )  cluster( cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(   1,     74) =      13.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
                                                  R-squared       =     0.4524
                                                  Adj R-squared   =     0.4047
                                                  Within R-sq.    =     0.0371
Number of clusters (cntyid)  =         75         Root MSE        =     1.2710

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .2135619    .058086     3.68   0.000      .097823    .3293008
                 _cons |   .9458922   .0524409    18.04   0.000     .8414015    1.050383
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons replace
Results\Table_2.doc
dir : seeout

.  
.   
. xi: reghdfe  lnmartyr1   lnurbanpop_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lna
> rea_Post  Zeng_all0_invdist_Post , absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(   8,     74) =       4.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.4673
                                                  Adj R-squared   =     0.4170
                                                  Within R-sq.    =     0.0633
Number of clusters (cntyid)  =         75         Root MSE        =     1.2578

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
       lnurbanpop_Post |  -.0456578   .1168494    -0.39   0.697    -.2784853    .1871697
          mainriv_Post |  -.2671877   .2397777    -1.11   0.269    -.7449553    .2105799
       dist2canal_Post |  -.1385291   .1760928    -0.79   0.434    -.4894016    .2123434
          lnwheat_Post |   2.232358   1.703064     1.31   0.194    -1.161071    5.625788
           lnrice_Post |  -1.520354   1.216859    -1.25   0.215    -3.944998    .9042896
            lnpop_Post |   .0259903   .2969883     0.09   0.931    -.5657717    .6177523
           lnarea_Post |    .867478   .4055907     2.14   0.036     .0593208    1.675635
Zeng_all0_invdist_Post |   .2008473   .0595963     3.37   0.001      .082099    .3195956
                 _cons |  -3.824691   3.975855    -0.96   0.339    -11.74675    4.097372
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

. 
.  
. 
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post  mainriv_Post dist2canal_
> Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post , absorb(year cntyid)  cluster(
>   cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  11,     74) =       6.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4771
                                                  Adj R-squared   =     0.4261
                                                  Within R-sq.    =     0.0806
Number of clusters (cntyid)  =         75         Root MSE        =     1.2480

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7375916   .2668154    -2.76   0.007    -1.269233   -.2059503
       lnurbanpop_Post |   .0521206   .1042167     0.50   0.618    -.1555358    .2597769
         lnjinshi_Post |   .3601757   .1454708     2.48   0.016     .0703189    .6500325
         lnquotas_Post |  -.2681027   .4165603    -0.64   0.522    -1.098117    .5619117
          mainriv_Post |  -.3585272   .2206177    -1.63   0.108    -.7981176    .0810632
       dist2canal_Post |   .0267347   .2038759     0.13   0.896    -.3794968    .4329663
          lnwheat_Post |    3.22485   1.746519     1.85   0.069    -.2551651    6.704866
           lnrice_Post |  -2.801058   1.215811    -2.30   0.024    -5.223615   -.3785014
            lnpop_Post |   .0393516   .3781392     0.10   0.917    -.7141072    .7928105
           lnarea_Post |   .7329297   .3959026     1.85   0.068    -.0559235    1.521783
Zeng_all0_invdist_Post |    .212477   .0582098     3.65   0.000     .0964914    .3284625
                 _cons |  -4.088389   4.708024    -0.87   0.388    -13.46933    5.292553
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

.  
. 
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post ,
>  absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       6.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4777
                                                  Adj R-squared   =     0.4256
                                                  Within R-sq.    =     0.0816
Number of clusters (cntyid)  =         75         Root MSE        =     1.2485

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7049407   .2642854    -2.67   0.009    -1.231541   -.1783405
       lnurbanpop_Post |    .029602    .113954     0.26   0.796    -.1974564    .2566604
         lnjinshi_Post |   .3342664   .1546342     2.16   0.034     .0261511    .6423817
         lnquotas_Post |   -.192861   .4344513    -0.44   0.658    -1.058524    .6728022
           route1_Post |  -.0881734   .3170126    -0.28   0.782    -.7198347    .5434879
     dist_nanjing_Post |  -.3833623   .5669867    -0.68   0.501    -1.513108    .7463833
          mainriv_Post |  -.4456899   .2968998    -1.50   0.138    -1.037276     .145896
       dist2canal_Post |   .3876836   .4654072     0.83   0.408    -.5396605    1.315028
          lnwheat_Post |   3.443393    1.70544     2.02   0.047     .0452296    6.841556
           lnrice_Post |  -2.970974   1.286773    -2.31   0.024    -5.534924   -.4070234
            lnpop_Post |   .0564806   .3597543     0.16   0.876    -.6603455    .7733067
           lnarea_Post |   .6937514   .3626769     1.91   0.060    -.0288982    1.416401
Zeng_all0_invdist_Post |   .2131379   .0591171     3.61   0.001     .0953444    .3309313
                 _cons |  -3.910144   4.723923    -0.83   0.410    -13.32277    5.502478
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

.  
. 
. 
. *** *** *** *** *** ***
. *** *** *** *** *** ***
. 
. xi: reghdfe  lnmartyr1   Zeng_all0_invdist_pc_Post , absorb(year cntyid)  cluster( cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(   1,     74) =       7.85
Statistics robust to heteroskedasticity           Prob > F        =     0.0065
                                                  R-squared       =     0.4466
                                                  Adj R-squared   =     0.3984
                                                  Within R-sq.    =     0.0268
Number of clusters (cntyid)  =         75         Root MSE        =     1.2777

                                             (Std. Err. adjusted for 75 clusters in cntyid)
-------------------------------------------------------------------------------------------
                          |               Robust
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_Post |   .0611623   .0218342     2.80   0.006     .0176567     .104668
                    _cons |   .9387503   .0713793    13.15   0.000     .7965239    1.080977
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

.  
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_invdist_pc_Po
> st , absorb(year cntyid )  cluster( cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       3.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0002
                                                  R-squared       =     0.4749
                                                  Adj R-squared   =     0.4225
                                                  Within R-sq.    =     0.0766
Number of clusters (cntyid)  =         75         Root MSE        =     1.2519

                                             (Std. Err. adjusted for 75 clusters in cntyid)
-------------------------------------------------------------------------------------------
                          |               Robust
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |   -.653642   .2855171    -2.29   0.025    -1.222547   -.0847367
          lnurbanpop_Post |   .0534559   .1251475     0.43   0.671    -.1959059    .3028178
            lnjinshi_Post |   .3863637   .1530153     2.53   0.014     .0814741    .6912533
            lnquotas_Post |  -.2645921   .4373717    -0.60   0.547    -1.136074      .60689
              route1_Post |   .0150395    .319362     0.05   0.963    -.6213031    .6513821
        dist_nanjing_Post |  -.2799039   .5574178    -0.50   0.617    -1.390583    .8307753
             mainriv_Post |  -.4153359   .3054772    -1.36   0.178    -1.024012    .1933406
          dist2canal_Post |   .3339425   .4640721     0.72   0.474    -.5907413    1.258626
             lnwheat_Post |     3.3591   1.750469     1.92   0.059    -.1287847    6.846985
              lnrice_Post |  -2.398038   1.308512    -1.83   0.071    -5.005305    .2092294
               lnpop_Post |   .2452513   .3760552     0.65   0.516     -.504055    .9945575
              lnarea_Post |   .6747845   .3706536     1.82   0.073    -.0637589    1.413328
Zeng_all0_invdist_pc_Post |   .0557689   .0231622     2.41   0.019     .0096172    .1019206
                    _cons |  -6.452596   5.112914    -1.26   0.211     -16.6403    3.735106
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

.  
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** 
. xi: reghdfe  lnmartyr1     Zeng_all0_Post , absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(   1,     74) =      12.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0009
                                                  R-squared       =     0.4524
                                                  Adj R-squared   =     0.4048
                                                  Within R-sq.    =     0.0371
Number of clusters (cntyid)  =         75         Root MSE        =     1.2710

                                  (Std. Err. adjusted for 75 clusters in cntyid)
--------------------------------------------------------------------------------
               |               Robust
     lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Zeng_all0_Post |   .1459895   .0420755     3.47   0.001     .0621521    .2298268
         _cons |   .9517028   .0538941    17.66   0.000     .8443165    1.059089
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

.  
.  **** *** *** 
. xi: reghdfe  lnmartyr1    capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_Post , absor
> b(year cntyid )  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       5.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4772
                                                  Adj R-squared   =     0.4250
                                                  Within R-sq.    =     0.0807
Number of clusters (cntyid)  =         75         Root MSE        =     1.2492

                                     (Std. Err. adjusted for 75 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6700051   .2581478    -2.60   0.011    -1.184376   -.1556344
  lnurbanpop_Post |    .012898   .1164853     0.11   0.912    -.2192041    .2450001
    lnjinshi_Post |   .3112826   .1606826     1.94   0.057    -.0088844    .6314497
    lnquotas_Post |  -.2156729   .4432698    -0.49   0.628    -1.098907    .6675614
      route1_Post |  -.0696883    .319512    -0.22   0.828    -.7063298    .5669532
dist_nanjing_Post |  -.3875764   .5587718    -0.69   0.490    -1.500954    .7258007
     mainriv_Post |  -.4371504   .2934998    -1.49   0.141    -1.021961    .1476607
  dist2canal_Post |   .3938562   .4600348     0.86   0.395    -.5227832    1.310496
     lnwheat_Post |   3.471026   1.733898     2.00   0.049     .0161593    6.925893
      lnrice_Post |  -2.954891   1.290718    -2.29   0.025    -5.526703   -.3830794
       lnpop_Post |   .0820618    .359155     0.23   0.820    -.6335702    .7976938
      lnarea_Post |    .727229   .3606214     2.02   0.047     .0086753    1.445783
   Zeng_all0_Post |    .146996   .0418096     3.52   0.001     .0636886    .2303034
            _cons |  -4.235122   4.709775    -0.90   0.371    -13.61955     5.14931
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

. 
. 
. *** *** *** 
. xi: reghdfe  lnmartyr1  Zeng_all0_pc_Post , absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(   1,     74) =       7.55
Statistics robust to heteroskedasticity           Prob > F        =     0.0076
                                                  R-squared       =     0.4495
                                                  Adj R-squared   =     0.4016
                                                  Within R-sq.    =     0.0320
Number of clusters (cntyid)  =         75         Root MSE        =     1.2744

                                     (Std. Err. adjusted for 75 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_pc_Post |   .0471883   .0171786     2.75   0.008     .0129591    .0814174
            _cons |   .9278596   .0767549    12.09   0.000     .7749222    1.080797
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

. 
.  
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_pc_Post , a
> bsorb(year cntyid  )  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       3.45
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.4756
                                                  Adj R-squared   =     0.4233
                                                  Within R-sq.    =     0.0779
Number of clusters (cntyid)  =         75         Root MSE        =     1.2511

                                     (Std. Err. adjusted for 75 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6406664   .2722356    -2.35   0.021    -1.183108   -.0982251
  lnurbanpop_Post |   .0289221   .1268541     0.23   0.820    -.2238402    .2816845
    lnjinshi_Post |   .3463195   .1618028     2.14   0.036     .0239205    .6687186
    lnquotas_Post |  -.3029375   .4381378    -0.69   0.491    -1.175946    .5700711
      route1_Post |   .0237919   .3231699     0.07   0.942    -.6201383     .667722
dist_nanjing_Post |  -.2859914   .5395299    -0.53   0.598    -1.361028    .7890453
     mainriv_Post |  -.3895546   .2955731    -1.32   0.192    -.9784969    .1993877
  dist2canal_Post |   .3513809   .4587839     0.77   0.446     -.562766    1.265528
     lnwheat_Post |   3.476429   1.776638     1.96   0.054    -.0636001    7.016457
      lnrice_Post |  -2.428809   1.251683    -1.94   0.056    -4.922842    .0652232
       lnpop_Post |    .254788   .3586997     0.71   0.480    -.4599368    .9695128
      lnarea_Post |   .7138959   .3710329     1.92   0.058    -.0254033    1.453195
Zeng_all0_pc_Post |   .0441034   .0169772     2.60   0.011     .0102756    .0779311
            _cons |  -6.702924   4.838777    -1.39   0.170     -16.3444    2.938549
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_2.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\Table_2.doc
dir : seeout

. 
. 
. 
. 
. 
. /*
> ********** The same results using xtreg  
> 
> 
> xtreg  lnmartyr1 i.year  Zeng_all0_invdist_Post , fe  cluster( cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons replace
>  
>   
> xtreg  lnmartyr1 i.year   lnurbanpop_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post ln
> area_Post  Zeng_all0_invdist_Post , fe   cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
> 
>  
> 
> xtreg  lnmartyr1 i.year  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post  mainriv_Post dist2canal
> _Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post , fe   cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
>  
> 
> xtreg  lnmartyr1 i.year  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjin
> g_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post 
> , fe   cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons append
>  
> 
> 
> *** *** *** *** *** ***
> *** *** *** *** *** ***
> 
> xtreg  lnmartyr1 i.year   Zeng_all0_invdist_pc_Post , fe   cluster( cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons append
>  
> xtreg  lnmartyr1 i.year  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjin
> g_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_invdist_pc_P
> ost , fe   cluster( cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons append
>  
> 
> 
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 
> *** *** *** 
> xtreg  lnmartyr1 i.year     Zeng_all0_Post , fe   cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
>  
>  **** *** *** 
> xtreg  lnmartyr1 i.year    capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanj
> ing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_Post , fe  
> cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
> 
> 
> *** *** *** 
> xtreg  lnmartyr1 i.year    Zeng_all0_pc_Post , fe   cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
> 
>  
> xtreg  lnmartyr1 i.year    capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanj
> ing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_pc_Post 
> , fe  cluster(  cntyid)
> outreg2 using Results\Table_2.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
> 
> */
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. 
. ******* Table B.1. I. The Impact of Elite Connections on Soldier Deaths: Checking Outliers
. 
. 
. do Programs\Appendix_Table_B1_I.do

. 
. ***************************************************************************************
. ************************** Table B.1. I. The Impact of Elite Connections on Soldier Deaths
. ************************** Checking Outliers
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************************************************************************
. ********************** regression
. 
. 
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post ,
>  absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       6.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4777
                                                  Adj R-squared   =     0.4256
                                                  Within R-sq.    =     0.0816
Number of clusters (cntyid)  =         75         Root MSE        =     1.2485

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7049407   .2642854    -2.67   0.009    -1.231541   -.1783405
       lnurbanpop_Post |    .029602    .113954     0.26   0.796    -.1974564    .2566604
         lnjinshi_Post |   .3342664   .1546342     2.16   0.034     .0261511    .6423817
         lnquotas_Post |   -.192861   .4344513    -0.44   0.658    -1.058524    .6728022
           route1_Post |  -.0881734   .3170126    -0.28   0.782    -.7198347    .5434879
     dist_nanjing_Post |  -.3833623   .5669867    -0.68   0.501    -1.513108    .7463833
          mainriv_Post |  -.4456899   .2968998    -1.50   0.138    -1.037276     .145896
       dist2canal_Post |   .3876836   .4654072     0.83   0.408    -.5396605    1.315028
          lnwheat_Post |   3.443393    1.70544     2.02   0.047     .0452296    6.841556
           lnrice_Post |  -2.970974   1.286773    -2.31   0.024    -5.534924   -.4070234
            lnpop_Post |   .0564806   .3597543     0.16   0.876    -.6603455    .7733067
           lnarea_Post |   .6937514   .3626769     1.91   0.060    -.0288982    1.416401
Zeng_all0_invdist_Post |   .2131379   .0591171     3.61   0.001     .0953444    .3309313
                 _cons |  -3.910144   4.723923    -0.83   0.410    -13.32277    5.502478
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B1_I.doc, keep(Zeng_all0_invdist_Post )  se  bdec(3) rdec(3) nocons repl
> ace 
Results\Appendix_Table_B1_I.doc
dir : seeout

. 
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post i
> f Zeng_all0_invdist<=10, absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,095
Absorbing 2 HDFE groups                           F(  13,     72) =       5.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4620
                                                  Adj R-squared   =     0.4079
                                                  Within R-sq.    =     0.0759
Number of clusters (cntyid)  =         73         Root MSE        =     1.2554

                                          (Std. Err. adjusted for 73 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.6099153   .2560073    -2.38   0.020    -1.120256    -.099574
       lnurbanpop_Post |    -.02439   .1282309    -0.19   0.850    -.2800136    .2312336
         lnjinshi_Post |   .2605462   .1586785     1.64   0.105    -.0557737     .576866
         lnquotas_Post |  -.0187816    .434665    -0.04   0.966    -.8852704    .8477072
           route1_Post |  -.0877576   .3425954    -0.26   0.799    -.7707091     .595194
     dist_nanjing_Post |  -.3786146   .5668613    -0.67   0.506    -1.508632    .7514028
          mainriv_Post |  -.3231095   .2926985    -1.10   0.273    -.9065932    .2603743
       dist2canal_Post |   .3660152   .4698905     0.78   0.439    -.5706943    1.302725
          lnwheat_Post |   3.775281   1.666999     2.26   0.027     .4521789    7.098382
           lnrice_Post |  -3.053982   1.324688    -2.31   0.024    -5.694701   -.4132642
            lnpop_Post |   -.018446   .3574847    -0.05   0.959    -.7310787    .6941866
           lnarea_Post |    .564817   .3472812     1.63   0.108    -.1274754    1.257109
Zeng_all0_invdist_Post |   .3799294    .115287     3.30   0.002      .150109    .6097499
                 _cons |  -2.787907   4.761801    -0.59   0.560    -12.28038     6.70457
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B1_I.doc, keep(Zeng_all0_invdist_Post )  se  bdec(3) rdec(3) nocons appe
> nd
Results\Appendix_Table_B1_I.doc
dir : seeout

. 
. 
. xi: reghdfe  lnmartyr1  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing
> _Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post  
> if Zeng_all0_invdist<=3, absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,050
Absorbing 2 HDFE groups                           F(  13,     69) =       3.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.4109
                                                  Adj R-squared   =     0.3509
                                                  Within R-sq.    =     0.0673
Number of clusters (cntyid)  =         70         Root MSE        =     1.2445

                                          (Std. Err. adjusted for 70 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.6587728   .2568843    -2.56   0.013    -1.171243   -.1463027
       lnurbanpop_Post |  -.1205517   .1231416    -0.98   0.331    -.3662125     .125109
         lnjinshi_Post |   .2565209   .1581439     1.62   0.109    -.0589675    .5720092
         lnquotas_Post |   -.128469   .3893935    -0.33   0.742    -.9052878    .6483499
           route1_Post |  -.0945184    .346513    -0.27   0.786    -.7857929    .5967561
     dist_nanjing_Post |  -.6968946    .561264    -1.24   0.219    -1.816586    .4227964
          mainriv_Post |  -.4316009   .2830832    -1.52   0.132    -.9963366    .1331347
       dist2canal_Post |   .7031363   .4647632     1.51   0.135    -.2240409    1.630313
          lnwheat_Post |   3.287863   1.689859     1.95   0.056    -.0833141     6.65904
           lnrice_Post |  -3.018924   1.242834    -2.43   0.018     -5.49831   -.5395387
            lnpop_Post |  -.0336883   .3442781    -0.10   0.922    -.7205043    .6531276
           lnarea_Post |   .7737271   .3226844     2.40   0.019     .1299893    1.417465
Zeng_all0_invdist_Post |   .5288147   .1959656     2.70   0.009     .1378741    .9197553
                 _cons |  -2.689067   4.353407    -0.62   0.539    -11.37388    5.995742
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        70          70           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B1_I.doc, keep(Zeng_all0_invdist_Post )  se  bdec(3) rdec(3) nocons appe
> nd
Results\Appendix_Table_B1_I.doc
dir : seeout

. 
. 
. 
. 
end of do-file

. 
. 
. ******* Table B.1. II. The Impact of Elite Connections on Soldier Deaths
. 
. 
. do Programs\Appendix_Table_B1_II.do

. 
. ***************************************************************************************
. ************************** Tab B.1. II. The impact of elite connections on soldier deaths
. ************************** sine transformation
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop   dist_nanjing
>  lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************************************************************************
. ********************** regression
. 
. xi: reghdfe  lnmartyr_sine  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Po
> st , absorb(year cntyid)  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       7.96
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4741
                                                  Adj R-squared   =     0.4216
                                                  Within R-sq.    =     0.0796
Number of clusters (cntyid)  =         75         Root MSE        =     1.4524

                                          (Std. Err. adjusted for 75 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
         lnmartyr_sine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.8186404   .2961344    -2.76   0.007    -1.408701   -.2285797
       lnurbanpop_Post |   .0461895   .1312206     0.35   0.726    -.2152731    .3076522
         lnjinshi_Post |   .4028383   .1792713     2.25   0.028     .0456324    .7600442
         lnquotas_Post |  -.2844044   .4968277    -0.57   0.569    -1.274355    .7055463
           route1_Post |  -.0825404   .3630549    -0.23   0.821    -.8059431    .6408624
     dist_nanjing_Post |  -.4281709   .6416998    -0.67   0.507    -1.706786     .850444
          mainriv_Post |  -.4757647   .3359559    -1.42   0.161    -1.145172    .1936421
       dist2canal_Post |   .4514163     .53234     0.85   0.399    -.6092942    1.512127
          lnwheat_Post |    3.99455    1.95533     2.04   0.045     .0984706    7.890629
           lnrice_Post |  -3.419109   1.471911    -2.32   0.023    -6.351956   -.4862614
            lnpop_Post |   .0854224   .4127677     0.21   0.837    -.7370352      .90788
           lnarea_Post |   .7837782   .4123397     1.90   0.061    -.0378266    1.605383
Zeng_all0_invdist_Post |    .238981    .064265     3.72   0.000     .1109302    .3670319
                 _cons |  -4.671027   5.367397    -0.87   0.387     -15.3658    6.023745
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_II.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons rep
> lace 
Results\\Appendix_Table_B1_II.doc
dir : seeout

.  
. 
.  
. xi: reghdfe  lnmartyr_sine  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_invdist_p
> c_Post , absorb(year cntyid )  cluster( cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       4.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4715
                                                  Adj R-squared   =     0.4187
                                                  Within R-sq.    =     0.0751
Number of clusters (cntyid)  =         75         Root MSE        =     1.4560

                                             (Std. Err. adjusted for 75 clusters in cntyid)
-------------------------------------------------------------------------------------------
                          |               Robust
            lnmartyr_sine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |  -.7618947   .3187817    -2.39   0.019    -1.397081   -.1267083
          lnurbanpop_Post |   .0726875   .1439976     0.50   0.615    -.2142341     .359609
            lnjinshi_Post |   .4609962    .176859     2.61   0.011      .108597    .8133955
            lnquotas_Post |  -.3649176   .4968301    -0.73   0.465    -1.354873    .6250379
              route1_Post |    .033277   .3667643     0.09   0.928    -.6975169    .7640708
        dist_nanjing_Post |  -.3118699   .6318654    -0.49   0.623    -1.570889    .9471494
             mainriv_Post |  -.4412727   .3458542    -1.28   0.206    -1.130402    .2478569
          dist2canal_Post |   .3911455   .5316794     0.74   0.464    -.6682488     1.45054
             lnwheat_Post |    3.90199    2.01774     1.93   0.057    -.1184434    7.922424
              lnrice_Post |  -2.775139   1.499918    -1.85   0.068     -5.76379    .2135118
               lnpop_Post |   .2975393   .4283339     0.69   0.489    -.5559347    1.151013
              lnarea_Post |   .7626536   .4215706     1.81   0.075    -.0773441    1.602651
Zeng_all0_invdist_pc_Post |   .0626719    .025814     2.43   0.018     .0112364    .1141075
                    _cons |  -7.531148   5.782878    -1.30   0.197    -19.05378    3.991488
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_II.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons
>  append
Results\\Appendix_Table_B1_II.doc
dir : seeout

.  
. 
.  
.  **** *** *** 
. xi: reghdfe  lnmartyr_sine    capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_n
> anjing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_Post , a
> bsorb(year cntyid )  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       6.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4737
                                                  Adj R-squared   =     0.4212
                                                  Within R-sq.    =     0.0790
Number of clusters (cntyid)  =         75         Root MSE        =     1.4529

                                     (Std. Err. adjusted for 75 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
    lnmartyr_sine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.7823258   .2892611    -2.70   0.008    -1.358691   -.2059605
  lnurbanpop_Post |   .0262293   .1339951     0.20   0.845    -.2407619    .2932204
    lnjinshi_Post |   .3755031   .1859099     2.02   0.047     .0050695    .7459367
    lnquotas_Post |  -.3098452   .5048294    -0.61   0.541     -1.31574    .6960493
      route1_Post |  -.0622503   .3655011    -0.17   0.865    -.7905271    .6660265
dist_nanjing_Post |  -.4328052   .6328012    -0.68   0.496    -1.693689    .8280787
     mainriv_Post |  -.4647779   .3309719    -1.40   0.164    -1.124254     .194698
  dist2canal_Post |    .458821    .526532     0.87   0.386    -.5903169    1.507959
     lnwheat_Post |   4.033379   1.987098     2.03   0.046     .0739998    7.992758
      lnrice_Post |  -3.400527   1.476324    -2.30   0.024    -6.342168   -.4588868
       lnpop_Post |   .1142634   .4112386     0.28   0.782    -.7051473    .9336742
      lnarea_Post |   .8222781    .410273     2.00   0.049     .0047912    1.639765
   Zeng_all0_Post |   .1661231    .045599     3.64   0.000      .075265    .2569811
            _cons |  -5.048694   5.341316    -0.95   0.348     -15.6915    5.594111
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_II.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_II.doc
dir : seeout

. 
. 
. *** *** ***  
.  
. xi: reghdfe  lnmartyr_sine  capital_Post  lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_pc_Post
>  , absorb(year cntyid  )  cluster(  cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,125
Absorbing 2 HDFE groups                           F(  13,     74) =       4.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.4723
                                                  Adj R-squared   =     0.4196
                                                  Within R-sq.    =     0.0764
Number of clusters (cntyid)  =         75         Root MSE        =     1.4549

                                     (Std. Err. adjusted for 75 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
    lnmartyr_sine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.7503766   .3053846    -2.46   0.016    -1.358869   -.1418845
  lnurbanpop_Post |   .0438324   .1456112     0.30   0.764    -.2463041     .333969
    lnjinshi_Post |   .4145162   .1865727     2.22   0.029     .0427619    .7862704
    lnquotas_Post |  -.4087607    .496377    -0.82   0.413    -1.397814     .580292
      route1_Post |    .043576   .3700984     0.12   0.907    -.6938611    .7810131
dist_nanjing_Post |  -.3175411   .6121024    -0.52   0.605    -1.537182    .9020996
     mainriv_Post |  -.4101365   .3338282    -1.23   0.223    -1.075304    .2550306
  dist2canal_Post |   .4108686   .5257349     0.78   0.437    -.6366811    1.458418
     lnwheat_Post |   4.043154   2.046291     1.98   0.052    -.0341705    8.120477
      lnrice_Post |  -2.803567   1.438005    -1.95   0.055    -5.668855    .0617205
       lnpop_Post |   .3102503   .4092197     0.76   0.451    -.5051377    1.125638
      lnarea_Post |   .8076027   .4222431     1.91   0.060    -.0337351    1.648941
Zeng_all0_pc_Post |   .0500239    .018766     2.67   0.009     .0126318     .087416
            _cons |  -7.853955   5.483188    -1.43   0.156    -18.77945    3.071535
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_II.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_II.doc
dir : seeout

. 
. 
end of do-file

. 
. 
. ******* Table B.1. III. The Impact of Elite Connections on Soldier Deaths: Spatial Clustering S.E.
. 
. 
. do Programs\Appendix_Table_B1_III.do

. 
. ***************************************************************************************
. ************************** Appendix Table B.1.III. arbitray clustering standard errors
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************************************************************************
. ********************** regression
. 
. 
. 
. 
. ************* 50 KM
. reg2hdfespatial lnmartyr1     Zeng_all0_invdist_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_co
> ord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .2135619   .0553997     3.85   0.000     .1048634    .3222604
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocon
> s replace
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  lnurbanpop_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post
>  lnarea_Post  Zeng_all0_invdist_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff
> (50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  lnurbanpop_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea
> _Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
       lnurbanpop_Post |  -.0456578   .1070376    -0.43   0.670    -.2556751    .1643596
          mainriv_Post |  -.2671877    .218825    -1.22   0.222    -.6965421    .1621667
       dist2canal_Post |  -.1385291   .1607045    -0.86   0.389    -.4538459    .1767876
          lnwheat_Post |   2.232358   1.589829     1.40   0.161      -.88703    5.351746
           lnrice_Post |  -1.520354   1.131397    -1.34   0.179    -3.740257    .6995482
            lnpop_Post |   .0259903   .2704232     0.10   0.923    -.5046044     .556585
           lnarea_Post |    .867478   .3729413     2.33   0.020     .1357335    1.599222
Zeng_all0_invdist_Post |   .2008473   .0573302     3.50   0.000     .0883602    .3133344
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocon
> s append 
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post mainriv_Post dist2canal
> _Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post, timevar(year) panelvar(cntyi
> d)    lat(y_coord) lon(x_coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post mainriv_Post dist2canal_Post l
> nwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7375916   .2418889    -3.05   0.002    -1.212201   -.2629823
       lnurbanpop_Post |   .0521206   .0978872     0.53   0.595    -.1399434    .2441845
         lnjinshi_Post |   .3601757   .1338343     2.69   0.007       .09758    .6227714
         lnquotas_Post |  -.2681027   .3869916    -0.69   0.489    -1.027417    .4912118
          mainriv_Post |  -.3585272   .2058826    -1.74   0.082    -.7624887    .0454343
       dist2canal_Post |   .0267347   .1863408     0.14   0.886    -.3388838    .3923533
          lnwheat_Post |    3.22485   1.634782     1.97   0.049     .0172519    6.432449
           lnrice_Post |  -2.801058   1.133567    -2.47   0.014    -5.025225   -.5768918
            lnpop_Post |   .0393516   .3461855     0.11   0.910    -.6398974    .7186007
           lnarea_Post |   .7329297   .3661021     2.00   0.046     .0146023    1.451257
Zeng_all0_invdist_Post |    .212477   .0558744     3.80   0.000     .1028461    .3221078
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocon
> s append 
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Pos
> t, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7049407   .2398299    -2.94   0.003    -1.175511   -.2343706
       lnurbanpop_Post |    .029602   .1062875     0.28   0.781    -.1789446    .2381486
         lnjinshi_Post |   .3342664    .141497     2.36   0.018     .0566351    .6118977
         lnquotas_Post |   -.192861   .4024216    -0.48   0.632    -.9824523    .5967304
           route1_Post |  -.0881734   .2933626    -0.30   0.764    -.6637801    .4874333
     dist_nanjing_Post |  -.3833623   .5282585    -0.73   0.468    -1.419858    .6531334
          mainriv_Post |  -.4456899   .2742665    -1.63   0.104     -.983828    .0924483
       dist2canal_Post |   .3876836   .4379232     0.89   0.376    -.4715653    1.246933
          lnwheat_Post |   3.443393    1.60403     2.15   0.032     .2961254     6.59066
           lnrice_Post |  -2.970974   1.199621    -2.48   0.013    -5.324749   -.6171979
            lnpop_Post |   .0564806   .3299418     0.17   0.864    -.5908982    .7038593
           lnarea_Post |   .6937514    .335656     2.07   0.039     .0351609    1.352342
Zeng_all0_invdist_Post |   .2131379   .0566564     3.76   0.000     .1019724    .3243034
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocon
> s append 
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. *************
. 
. reg2hdfespatial lnmartyr1    Zeng_all0_invdist_pc_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_
> coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_Post |   .0611623   .0201812     3.03   0.002     .0215653    .1007594
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) no
> cons append 
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_pc_
> Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |   -.653642   .2582601    -2.53   0.012    -1.160374     -.14691
          lnurbanpop_Post |   .0534559   .1162348     0.46   0.646    -.1746084    .2815202
            lnjinshi_Post |   .3863637   .1401224     2.76   0.006     .1114295    .6612979
            lnquotas_Post |  -.2645921   .4043591    -0.65   0.513    -1.057985    .5288008
              route1_Post |   .0150395    .294611     0.05   0.959    -.5630167    .5930957
        dist_nanjing_Post |  -.2799039   .5214229    -0.54   0.592    -1.302988    .7431799
             mainriv_Post |  -.4153359   .2806835    -1.48   0.139    -.9660649     .135393
          dist2canal_Post |   .3339425   .4378822     0.76   0.446     -.525226    1.193111
             lnwheat_Post |     3.3591   1.645507     2.04   0.041     .1304508     6.58775
              lnrice_Post |  -2.398038   1.219708    -1.97   0.050    -4.791225   -.0048501
               lnpop_Post |   .2452513   .3439828     0.71   0.476    -.4296773    .9201798
              lnarea_Post |   .6747845   .3432673     1.97   0.050     .0012599    1.348309
Zeng_all0_invdist_pc_Post |   .0557689   .0217292     2.57   0.010     .0131341    .0984037
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) no
> cons append 
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. *** *** *** 
. reg2hdfespatial  lnmartyr1     Zeng_all0_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) d
> istcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
--------------------------------------------------------------------------------
     lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Zeng_all0_Post |   .1459895   .0398061     3.67   0.000     .0678869     .224092
--------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
.  
. *** *** *** 
. reg2hdfespatial  lnmartyr1   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_Post , 
> timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6700051   .2343719    -2.86   0.004    -1.129866    -.210144
  lnurbanpop_Post |    .012898   .1084406     0.12   0.905    -.1998733    .2256693
    lnjinshi_Post |   .3112826   .1469601     2.12   0.034     .0229322     .599633
    lnquotas_Post |  -.2156729   .4097748    -0.53   0.599    -1.019692    .5883461
      route1_Post |  -.0696883   .2954281    -0.24   0.814    -.6493477    .5099711
dist_nanjing_Post |  -.3875764   .5212064    -0.74   0.457    -1.410235    .6350825
     mainriv_Post |  -.4371504   .2713609    -1.61   0.107    -.9695875    .0952868
  dist2canal_Post |   .3938562    .433412     0.91   0.364    -.4565413    1.244254
     lnwheat_Post |   3.471026   1.628892     2.13   0.033     .2749781    6.667074
      lnrice_Post |  -2.954891   1.202092    -2.46   0.014    -5.313515    -.596268
       lnpop_Post |   .0820618   .3293137     0.25   0.803    -.5640844     .728208
      lnarea_Post |    .727229   .3336137     2.18   0.029     .0726458    1.381812
   Zeng_all0_Post |    .146996   .0398696     3.69   0.000     .0687679    .2252241
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. *** *** *** 
. 
. reg2hdfespatial lnmartyr1  Zeng_all0_pc_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) di
> stcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_pc_Post |   .0471883    .015724     3.00   0.003     .0163366      .07804
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons ap
> pend
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
.  
. reg2hdfespatial  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanj
> ing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_pc_Post  
> , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(50) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6406664    .246215    -2.60   0.009    -1.123765   -.1575682
  lnurbanpop_Post |   .0289221   .1176467     0.25   0.806    -.2019123    .2597566
    lnjinshi_Post |   .3463195   .1477693     2.34   0.019     .0563815    .6362576
    lnquotas_Post |  -.3029375   .4052667    -0.75   0.455    -1.098111    .4922362
      route1_Post |   .0237919   .2984235     0.08   0.936    -.5617449    .6093286
dist_nanjing_Post |  -.2859914   .5060608    -0.57   0.572    -1.278933    .7069503
     mainriv_Post |  -.3895546    .271989    -1.43   0.152    -.9232241    .1441149
  dist2canal_Post |   .3513809   .4334524     0.81   0.418    -.4990958    1.201858
     lnwheat_Post |   3.476429   1.669154     2.08   0.038     .2013823    6.751475
      lnrice_Post |  -2.428809   1.171064    -2.07   0.038    -4.726554   -.1310647
       lnpop_Post |    .254788   .3287374     0.78   0.438    -.3902276    .8998035
      lnarea_Post |   .7138959   .3435713     2.08   0.038     .0397748    1.388017
Zeng_all0_pc_Post |   .0441034   .0157957     2.79   0.005     .0131107     .075096
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_50KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons ap
> pend
Results\Appendix_Table_B1_III_50KM.doc
dir : seeout

. 
. 
. 
. 
. 
. ********************* 100 KM
. 
. reg2hdfespatial lnmartyr1     Zeng_all0_invdist_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_co
> ord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .2135619   .0569078     3.75   0.000     .1019044    .3252194
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns replace
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  lnurbanpop_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post
>  lnarea_Post  Zeng_all0_invdist_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff
> (100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  lnurbanpop_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea
> _Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
       lnurbanpop_Post |  -.0456578   .1071047    -0.43   0.670    -.2558068    .1644912
          mainriv_Post |  -.2671877   .2229211    -1.20   0.231    -.7045789    .1702035
       dist2canal_Post |  -.1385291    .166291    -0.83   0.405    -.4648071    .1877489
          lnwheat_Post |   2.232358   1.622748     1.38   0.169    -.9516196    5.416336
           lnrice_Post |  -1.520354   1.184371    -1.28   0.200    -3.844196    .8034872
            lnpop_Post |   .0259903   .2782511     0.09   0.926    -.5199634     .571944
           lnarea_Post |    .867478   .3779478     2.30   0.022     .1259104    1.609046
Zeng_all0_invdist_Post |   .2008473   .0587257     3.42   0.001     .0856222    .3160724
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post mainriv_Post dist2canal
> _Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post, timevar(year) panelvar(cntyi
> d)    lat(y_coord) lon(x_coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post mainriv_Post dist2canal_Post l
> nwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7375916   .2398647    -3.08   0.002    -1.208229    -.266954
       lnurbanpop_Post |   .0521206   .1014398     0.51   0.607     -.146914    .2511552
         lnjinshi_Post |   .3601757   .1389784     2.59   0.010     .0874868    .6328646
         lnquotas_Post |  -.2681027   .4099458    -0.65   0.513    -1.072456    .5362503
          mainriv_Post |  -.3585272    .208723    -1.72   0.086    -.7680618    .0510074
       dist2canal_Post |   .0267347   .1940918     0.14   0.890     -.354092    .4075615
          lnwheat_Post |    3.22485   1.661683     1.94   0.053    -.0355304    6.485231
           lnrice_Post |  -2.801058   1.178915    -2.38   0.018    -5.114203   -.4879138
            lnpop_Post |   .0393516   .3550738     0.11   0.912    -.6573371    .7360404
           lnarea_Post |   .7329297   .3740069     1.96   0.050    -.0009076    1.466767
Zeng_all0_invdist_Post |    .212477   .0573485     3.71   0.000     .0999537    .3250003
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Pos
> t, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7049407   .2372957    -2.97   0.003    -1.170538   -.2393429
       lnurbanpop_Post |    .029602   .1093664     0.27   0.787    -.1849858    .2441897
         lnjinshi_Post |   .3342664    .145243     2.30   0.022     .0492851    .6192477
         lnquotas_Post |   -.192861   .4149059    -0.46   0.642    -1.006948    .6212258
           route1_Post |  -.0881734   .2867732    -0.31   0.759     -.650851    .4745043
     dist_nanjing_Post |  -.3833623   .5555415    -0.69   0.490     -1.47339    .7066654
          mainriv_Post |  -.4456899   .2755428    -1.62   0.106    -.9863322    .0949525
       dist2canal_Post |   .3876836   .4732228     0.82   0.413    -.5408267    1.316194
          lnwheat_Post |   3.443393   1.627271     2.12   0.035      .250524    6.636261
           lnrice_Post |  -2.970974   1.241891    -2.39   0.017    -5.407688   -.5342597
            lnpop_Post |   .0564806    .336774     0.17   0.867    -.6043036    .7172648
           lnarea_Post |   .6937514   .3421799     2.03   0.043     .0223603    1.365143
Zeng_all0_invdist_Post |   .2131379   .0578224     3.69   0.000     .0996846    .3265911
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. *************
. 
. reg2hdfespatial lnmartyr1    Zeng_all0_invdist_pc_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_
> coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_Post |   .0611623   .0204375     2.99   0.003     .0210624    .1012622
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) n
> ocons append 
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_pc_
> Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |   -.653642   .2564199    -2.55   0.011    -1.156763   -.1505206
          lnurbanpop_Post |   .0534559   .1188499     0.45   0.653    -.1797394    .2866513
            lnjinshi_Post |   .3863637   .1435455     2.69   0.007     .1047132    .6680142
            lnquotas_Post |  -.2645921   .4157644    -0.64   0.525    -1.080363    .5511791
              route1_Post |   .0150395   .2889368     0.05   0.958    -.5518834    .5819623
        dist_nanjing_Post |  -.2799039   .5481922    -0.51   0.610    -1.355512    .7957038
             mainriv_Post |  -.4153359   .2818373    -1.47   0.141    -.9683288    .1376569
          dist2canal_Post |   .3339425   .4724639     0.71   0.480    -.5930787    1.260964
             lnwheat_Post |     3.3591   1.664299     2.02   0.044     .0935804     6.62462
              lnrice_Post |  -2.398038   1.254943    -1.91   0.056     -4.86036     .064285
               lnpop_Post |   .2452513   .3504815     0.70   0.484    -.4424284    .9329309
              lnarea_Post |   .6747845   .3492258     1.93   0.054    -.0104312        1.36
Zeng_all0_invdist_pc_Post |   .0557689   .0218141     2.56   0.011     .0129674    .0985704
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) n
> ocons append 
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** 
. reg2hdfespatial  lnmartyr1     Zeng_all0_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) d
> istcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
--------------------------------------------------------------------------------
     lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Zeng_all0_Post |   .1459895   .0407097     3.59   0.000     .0661139     .225865
--------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons appen
> d
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
.  
. *** *** *** 
. reg2hdfespatial  lnmartyr1   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_Post , 
> timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6700051   .2316749    -2.89   0.004    -1.124574   -.2154359
  lnurbanpop_Post |    .012898   .1115638     0.12   0.908    -.2060012    .2317972
    lnjinshi_Post |   .3112826     .15124     2.06   0.040     .0145347    .6080305
    lnquotas_Post |  -.2156729   .4218491    -0.51   0.609    -1.043383    .6120371
      route1_Post |  -.0696883   .2886599    -0.24   0.809    -.6360678    .4966912
dist_nanjing_Post |  -.3875764   .5498031    -0.70   0.481    -1.466345    .6911921
     mainriv_Post |  -.4371504    .273323    -1.60   0.110    -.9734372    .0991365
  dist2canal_Post |   .3938562   .4702485     0.84   0.402    -.5288183    1.316531
     lnwheat_Post |   3.471026   1.653464     2.10   0.036     .2267641    6.715288
      lnrice_Post |  -2.954891   1.243148    -2.38   0.018    -5.394073   -.5157103
       lnpop_Post |   .0820618   .3363237     0.24   0.807    -.5778388    .7419623
      lnarea_Post |    .727229    .340495     2.14   0.033     .0591439    1.395314
   Zeng_all0_Post |    .146996   .0407679     3.61   0.000     .0670053    .2269867
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons appen
> d
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. *** *** *** 
. 
. reg2hdfespatial lnmartyr1  Zeng_all0_pc_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) di
> stcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_pc_Post |   .0471883   .0157908     2.99   0.003     .0162054    .0781711
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons a
> ppend
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
.  
. reg2hdfespatial  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanj
> ing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_pc_Post  
> , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(100) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6406664   .2447747    -2.62   0.009    -1.120939   -.1603941
  lnurbanpop_Post |   .0289221   .1203348     0.24   0.810    -.2071868    .2650311
    lnjinshi_Post |   .3463195   .1518889     2.28   0.023     .0482983    .6443408
    lnquotas_Post |  -.3029375   .4166941    -0.73   0.467    -1.120533    .5146578
      route1_Post |   .0237919    .292735     0.08   0.935    -.5505833     .598167
dist_nanjing_Post |  -.2859914    .534366    -0.54   0.593    -1.334471     .762488
     mainriv_Post |  -.3895546   .2740546    -1.42   0.155     -.927277    .1481678
  dist2canal_Post |   .3513809   .4692816     0.75   0.454    -.5693962    1.272158
     lnwheat_Post |   3.476429    1.68717     2.06   0.040     .1660338    6.786823
      lnrice_Post |  -2.428809   1.208312    -2.01   0.045    -4.799638   -.0579803
       lnpop_Post |    .254788   .3355588     0.76   0.448    -.4036118    .9131878
      lnarea_Post |   .7138959   .3498545     2.04   0.042     .0274466    1.400345
Zeng_all0_pc_Post |   .0441034   .0159261     2.77   0.006     .0128548    .0753519
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_100KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons a
> ppend
Results\Appendix_Table_B1_III_100KM.doc
dir : seeout

. 
. 
. 
. 
. 
. ********************* 200 KM
. 
. reg2hdfespatial lnmartyr1     Zeng_all0_invdist_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_co
> ord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .2135619   .0551959     3.87   0.000     .1052633    .3218605
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns replace
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  lnurbanpop_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post
>  lnarea_Post  Zeng_all0_invdist_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff
> (200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  lnurbanpop_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea
> _Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
       lnurbanpop_Post |  -.0456578   .1050936    -0.43   0.664    -.2518608    .1605453
          mainriv_Post |  -.2671877   .2290926    -1.17   0.244    -.7166879    .1823125
       dist2canal_Post |  -.1385291   .1701258    -0.81   0.416    -.4723313     .195273
          lnwheat_Post |   2.232358    1.65733     1.35   0.178    -1.019472    5.484188
           lnrice_Post |  -1.520354   1.193157    -1.27   0.203    -3.861436    .8207273
            lnpop_Post |   .0259903   .2917624     0.09   0.929    -.5464739    .5984545
           lnarea_Post |    .867478   .3834889     2.26   0.024     .1150382    1.619918
Zeng_all0_invdist_Post |   .2008473   .0580363     3.46   0.001     .0869748    .3147198
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post mainriv_Post dist2canal
> _Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Post, timevar(year) panelvar(cntyi
> d)    lat(y_coord) lon(x_coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post mainriv_Post dist2canal_Post l
> nwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7375916   .2327158    -3.17   0.002    -1.194202   -.2809809
       lnurbanpop_Post |   .0521206   .1018674     0.51   0.609     -.147753    .2519941
         lnjinshi_Post |   .3601757    .140756     2.56   0.011      .083999    .6363524
         lnquotas_Post |  -.2681027   .4278775    -0.63   0.531    -1.107639    .5714338
          mainriv_Post |  -.3585272   .2043697    -1.75   0.080    -.7595201    .0424657
       dist2canal_Post |   .0267347   .1984959     0.13   0.893    -.3627333    .4162028
          lnwheat_Post |    3.22485   1.700981     1.90   0.058     -.112638    6.562339
           lnrice_Post |  -2.801058   1.192086    -2.35   0.019    -5.140046   -.4620706
            lnpop_Post |   .0393516   .3719019     0.11   0.916    -.6903555    .7690588
           lnarea_Post |   .7329297   .3761647     1.95   0.052    -.0051415    1.471001
Zeng_all0_invdist_Post |    .212477   .0581038     3.66   0.000     .0984718    .3264821
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_Pos
> t, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
----------------------------------------------------------------------------------------
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -.7049407   .2273689    -3.10   0.002    -1.151061   -.2588203
       lnurbanpop_Post |    .029602   .1085148     0.27   0.785    -.1833149    .2425189
         lnjinshi_Post |   .3342664   .1458503     2.29   0.022     .0480935    .6204393
         lnquotas_Post |   -.192861   .4179869    -0.46   0.645    -1.012993     .627271
           route1_Post |  -.0881734   .2907832    -0.30   0.762     -.658719    .4823722
     dist_nanjing_Post |  -.3833623   .5909338    -0.65   0.517    -1.542833    .7761086
          mainriv_Post |  -.4456899   .2719477    -1.64   0.102    -.9792783    .0878986
       dist2canal_Post |   .3876836   .5093304     0.76   0.447    -.6116734    1.387041
          lnwheat_Post |   3.443393   1.657196     2.08   0.038     .1918097    6.694976
           lnrice_Post |  -2.970974   1.263016    -2.35   0.019    -5.449137   -.4928105
            lnpop_Post |   .0564806   .3510134     0.16   0.872    -.6322427    .7452039
           lnarea_Post |   .6937514   .3411832     2.03   0.042      .024316    1.363187
Zeng_all0_invdist_Post |   .2131379   .0579536     3.68   0.000     .0994272    .3268486
----------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) noco
> ns append 
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. 
. *************
. 
. reg2hdfespatial lnmartyr1    Zeng_all0_invdist_pc_Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_
> coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_Post |   .0611623   .0201919     3.03   0.003     .0215443    .1007804
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) n
> ocons append 
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. reg2hdfespatial lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanji
> ng_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  Zeng_all0_invdist_pc_
> Post, timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_invdist_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-------------------------------------------------------------------------------------------
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |   -.653642   .2433013    -2.69   0.007    -1.131023   -.1762607
          lnurbanpop_Post |   .0534559   .1174022     0.46   0.649    -.1768988    .2838107
            lnjinshi_Post |   .3863637   .1439823     2.68   0.007     .1038561    .6688712
            lnquotas_Post |  -.2645921   .4158104    -0.64   0.525    -1.080453    .5512693
              route1_Post |   .0150395   .2980786     0.05   0.960    -.5698205    .5998994
        dist_nanjing_Post |  -.2799039   .5812818    -0.48   0.630    -1.420437     .860629
             mainriv_Post |  -.4153359   .2758239    -1.51   0.132      -.95653    .1258581
          dist2canal_Post |   .3339425   .5063288     0.66   0.510     -.659525     1.32741
             lnwheat_Post |     3.3591   1.701728     1.97   0.049     .0201398    6.698061
              lnrice_Post |  -2.398038   1.265592    -1.89   0.058    -4.881255    .0851796
               lnpop_Post |   .2452513    .363142     0.68   0.500    -.4672696    .9577721
              lnarea_Post |   .6747845   .3469032     1.95   0.052     -.005874    1.355443
Zeng_all0_invdist_pc_Post |   .0557689   .0218216     2.56   0.011     .0129528    .0985851
-------------------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_invdist_pc_Post)  se  bdec(3) rdec(3) n
> ocons append 
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** 
. reg2hdfespatial  lnmartyr1     Zeng_all0_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) d
> istcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
--------------------------------------------------------------------------------
     lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Zeng_all0_Post |   .1459895   .0394447     3.70   0.000     .0685959     .223383
--------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons appen
> d
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
.  
. *** *** *** 
. reg2hdfespatial  lnmartyr1   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nan
> jing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_Post , 
> timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6700051   .2223076    -3.01   0.003    -1.106195   -.2338154
  lnurbanpop_Post |    .012898   .1105774     0.12   0.907    -.2040659    .2298619
    lnjinshi_Post |   .3112826   .1524774     2.04   0.041     .0121067    .6104585
    lnquotas_Post |  -.2156729   .4256498    -0.51   0.612     -1.05084    .6194944
      route1_Post |  -.0696883   .2922195    -0.24   0.812    -.6430522    .5036756
dist_nanjing_Post |  -.3875764   .5867894    -0.66   0.509    -1.538916    .7637629
     mainriv_Post |  -.4371504   .2692111    -1.62   0.105    -.9653693    .0910686
  dist2canal_Post |   .3938562   .5076934     0.78   0.438    -.6022889    1.390001
     lnwheat_Post |   3.471026   1.678632     2.07   0.039     .1773824     6.76467
      lnrice_Post |  -2.954891   1.261564    -2.34   0.019    -5.430206   -.4795764
       lnpop_Post |   .0820618   .3508056     0.23   0.815    -.6062537    .7703772
      lnarea_Post |    .727229   .3403913     2.14   0.033     .0593474    1.395111
   Zeng_all0_Post |    .146996   .0408655     3.60   0.000     .0668138    .2271782
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons appen
> d
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. *** *** *** 
. 
. reg2hdfespatial lnmartyr1  Zeng_all0_pc_Post , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) di
> stcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_pc_Post |   .0471883   .0153449     3.08   0.002     .0170805    .0772961
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons a
> ppend
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
.  
. reg2hdfespatial  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanj
> ing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   Zeng_all0_pc_Post  
> , timevar(year) panelvar(cntyid)    lat(y_coord) lon(x_coord) distcutoff(200) lagcutoff(20) 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: lnmartyr1
INDEPENDANT VARIABLES:  capital_Post lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post 
> mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post Zeng_all0_pc_Post
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 20 PERIODS
-----------------------------------------------------------------------------------
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -.6406664   .2334976    -2.74   0.006    -1.098812   -.1825209
  lnurbanpop_Post |   .0289221   .1193687     0.24   0.809    -.2052911    .2631354
    lnjinshi_Post |   .3463195    .152832     2.27   0.024     .0464479    .6461911
    lnquotas_Post |  -.3029375   .4180867    -0.72   0.469    -1.123265    .5173903
      route1_Post |   .0237919   .3013333     0.08   0.937     -.567454    .6150377
dist_nanjing_Post |  -.2859914    .570015    -0.50   0.616    -1.404418    .8324348
     mainriv_Post |  -.3895546   .2676453    -1.46   0.146    -.9147013    .1355921
  dist2canal_Post |   .3513809   .5047516     0.70   0.486     -.638992    1.341754
     lnwheat_Post |   3.476429   1.711298     2.03   0.042     .1186923    6.834165
      lnrice_Post |  -2.428809    1.21895    -1.99   0.047     -4.82051   -.0371083
       lnpop_Post |    .254788    .349771     0.73   0.466    -.4314975    .9410734
      lnarea_Post |   .7138959   .3483159     2.05   0.041     .0304654    1.397326
Zeng_all0_pc_Post |   .0441034   .0159995     2.76   0.006     .0127108    .0754959
-----------------------------------------------------------------------------------
D:\Dropbox\Xiangjun\FinalFiles\Replication

. outreg2 using Results\Appendix_Table_B1_III_200KM.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons a
> ppend
Results\Appendix_Table_B1_III_200KM.doc
dir : seeout

. 
. 
. 
end of do-file

. 
. 
. ******* Table B.1. IV. The Impact of Elite Connections on Soldier Deaths: Controls X Year FE
. 
. 
. do Programs\Appendix_Table_B1_IV.do

. 
. 
. ***************************************************************************************
. ************************** Appendix Table B.1.IV. Additional Fixed effects
. ****************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************** ********************** ********************** **********************
. ********************** gen year dummies
. 
. tab year, gen(year)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1850 |         75        6.67        6.67
       1851 |         75        6.67       13.33
       1852 |         75        6.67       20.00
       1853 |         75        6.67       26.67
       1854 |         75        6.67       33.33
       1855 |         75        6.67       40.00
       1856 |         75        6.67       46.67
       1857 |         75        6.67       53.33
       1858 |         75        6.67       60.00
       1859 |         75        6.67       66.67
       1860 |         75        6.67       73.33
       1861 |         75        6.67       80.00
       1862 |         75        6.67       86.67
       1863 |         75        6.67       93.33
       1864 |         75        6.67      100.00
------------+-----------------------------------
      Total |      1,125      100.00

. local r=1 

. while `r'<16 {
  2. local s=`r'+1849
  3. rename year`r' yr`s'
  4. local r=`r'+1
  5. }

. 
. **
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. foreach x of varlist yr1850-yr1852 yr1854-yr1864 {
  3. gen `y'_`x'=`y'*`x'
  4. }
  5. }

. 
. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
.  
. ********************************************************************************
. ********************** regression
. 
. *** *** *** 
. *** *** ***  
. *** *** ***  controls X year FE 
. *** *** *** 
. *** *** *** 
. 
.  
. xi: reghdfe  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_
> Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     Zeng_all0_invdist_Post
>  , absorb(year cntyid prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     13) =    2722.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6689
                                                  Adj R-squared   =     0.5398
Number of clusters (prefid)  =         14         Within R-sq.    =     0.1130
Number of clusters (cntyid)  =         74         Root MSE        =     1.1202

                                   (Std. Err. adjusted for 14 clusters in prefid cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.432147   .4515963    -3.17   0.007    -2.407761   -.4565321
       lnurbanpop_Post |   .4336873   .1287973     3.37   0.005     .1554376     .711937
         lnjinshi_Post |   .3889764   .1370676     2.84   0.014     .0928599    .6850929
         lnquotas_Post |  -.8923757   .3876607    -2.30   0.039    -1.729866   -.0548857
           route1_Post |  -.1870466   .0701287    -2.67   0.019    -.3385505   -.0355427
     dist_nanjing_Post |  -1.389248   .8120499    -1.71   0.111    -3.143575    .3650788
          mainriv_Post |  -.7111534   .2342116    -3.04   0.010    -1.217137   -.2051699
       dist2canal_Post |    .551294   .9408525     0.59   0.568    -1.481294    2.583882
          lnwheat_Post |   5.029848   2.171359     2.32   0.037      .338911    9.720785
           lnrice_Post |   -4.04189   1.090488    -3.71   0.003    -6.397745   -1.686035
            lnpop_Post |  -.0872555   .3775549    -0.23   0.821    -.9029133    .7284023
           lnarea_Post |   .8448603   .3785877     2.23   0.044     .0269712    1.662749
Zeng_all0_invdist_Post |   .2614323   .0650554     4.02   0.001     .1208887    .4019759
                 _cons |   .1755985   4.937185     0.04   0.972    -10.49054    10.84174
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons rep
> lace 
Results\\Appendix_Table_B1_IV.doc
dir : seeout

. 
. 
. 
. xi: reghdfe  lnmartyr1  capital_yr* lnurbanpop_yr*  lnjinshi_yr*  lnquotas_yr* route1_yr*  dist_nanjing_yr* m
> ainriv_yr*  lnwheat_yr* lnrice_yr* lnpop_yr* lnarea_yr*   Zeng_all0_invdist_Post , absorb(year cntyid  prefid
> Xyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F( 155,     13) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7365
                                                  Adj R-squared   =     0.5546
Number of clusters (prefid)  =         14         Within R-sq.    =     0.2943
Number of clusters (cntyid)  =         74         Root MSE        =     1.1021

                                   (Std. Err. adjusted for 14 clusters in prefid cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
        capital_yr1850 |  -.4097634   .5133972    -0.80   0.439    -1.518891    .6993638
        capital_yr1851 |  -.2484227   .4727179    -0.53   0.608    -1.269668    .7728223
        capital_yr1852 |   .1947214   .9575611     0.20   0.842    -1.873964    2.263406
        capital_yr1854 |  -.2781386   1.163314    -0.24   0.815    -2.791327    2.235049
        capital_yr1855 |  -.9276421   .5821173    -1.59   0.135     -2.18523     .329946
        capital_yr1856 |  -1.131734   .5053794    -2.24   0.043     -2.22354   -.0399285
        capital_yr1857 |  -1.509711   .5858905    -2.58   0.023    -2.775451   -.2439718
        capital_yr1858 |  -1.534742    .454656    -3.38   0.005    -2.516966   -.5525172
        capital_yr1859 |   -1.67294   .5647111    -2.96   0.011    -2.892924   -.4529557
        capital_yr1860 |  -1.754246   .4827984    -3.63   0.003    -2.797268   -.7112234
        capital_yr1861 |  -2.818008   .9037057    -3.12   0.008    -4.770345   -.8656699
        capital_yr1862 |  -1.670107   .5920366    -2.82   0.014    -2.949125   -.3910899
        capital_yr1863 |  -2.316771   .5244949    -4.42   0.001    -3.449873   -1.183668
        capital_yr1864 |  -1.981421    .431792    -4.59   0.001     -2.91425   -1.048591
     lnurbanpop_yr1850 |   .2791304   .2397642     1.16   0.265    -.2388486    .7971094
     lnurbanpop_yr1851 |   .1683175   .2015215     0.84   0.419    -.2670432    .6036781
     lnurbanpop_yr1852 |   .2127898   .3775463     0.56   0.583    -.6028493    1.028429
     lnurbanpop_yr1854 |   .1988593   .2964387     0.67   0.514    -.4415577    .8392762
     lnurbanpop_yr1855 |   .4222278   .2914308     1.45   0.171    -.2073702    1.051826
     lnurbanpop_yr1856 |   .2207461   .3093918     0.71   0.488    -.4476542    .8891465
     lnurbanpop_yr1857 |   .5276067    .172771     3.05   0.009     .1543577    .9008556
     lnurbanpop_yr1858 |   .6339161   .1910698     3.32   0.006     .2211349    1.046697
     lnurbanpop_yr1859 |   .5718022   .1881473     3.04   0.009     .1653346    .9782697
     lnurbanpop_yr1860 |   .6590065   .2141007     3.08   0.009       .19647    1.121543
     lnurbanpop_yr1861 |   1.013229   .3763622     2.69   0.018     .2001475     1.82631
     lnurbanpop_yr1862 |   .7272802    .251428     2.89   0.013      .184103    1.270457
     lnurbanpop_yr1863 |   .9233471   .3312512     2.79   0.015     .2077224    1.638972
     lnurbanpop_yr1864 |   .9291035   .2132794     4.36   0.001     .4683415    1.389866
       lnjinshi_yr1850 |   .2607736   .3239514     0.80   0.435    -.4390808    .9606279
       lnjinshi_yr1851 |   .1771224    .311886     0.57   0.580    -.4966664    .8509112
       lnjinshi_yr1852 |  -.3651996   .2547664    -1.43   0.175     -.915589    .1851897
       lnjinshi_yr1854 |   .1496308   .1634917     0.92   0.377    -.2035716    .5028331
       lnjinshi_yr1855 |   .1358702   .2600919     0.52   0.610    -.4260243    .6977646
       lnjinshi_yr1856 |   .4230874   .2978192     1.42   0.179     -.220312    1.066487
       lnjinshi_yr1857 |   .6061672   .3438705     1.76   0.101    -.1367198    1.349054
       lnjinshi_yr1858 |   .2897488   .2353171     1.23   0.240     -.218623    .7981205
       lnjinshi_yr1859 |   .3245451   .2372279     1.37   0.194    -.1879546    .8370447
       lnjinshi_yr1860 |   .3645943   .2244546     1.62   0.128    -.1203103     .849499
       lnjinshi_yr1861 |   .5560925   .3709697     1.50   0.158    -.2453389    1.357524
       lnjinshi_yr1862 |   .5489456   .4557269     1.20   0.250    -.4355925    1.533484
       lnjinshi_yr1863 |   .5400647   .3858499     1.40   0.185    -.2935134    1.373643
       lnjinshi_yr1864 |   .5726001   .4080557     1.40   0.184    -.3089506    1.454151
       lnquotas_yr1850 |   -.603631    .616995    -0.98   0.346    -1.936568    .7293056
       lnquotas_yr1851 |  -.7137533   .5453759    -1.31   0.213    -1.891966    .4644597
       lnquotas_yr1852 |    .043315    .710833     0.06   0.952    -1.492346    1.578976
       lnquotas_yr1854 |   .3080843   .9220578     0.33   0.744    -1.683901    2.300069
       lnquotas_yr1855 |  -1.497103   .6927454    -2.16   0.050    -2.993689   -.0005178
       lnquotas_yr1856 |  -.5355455   1.501512    -0.36   0.727    -3.779365    2.708274
       lnquotas_yr1857 |  -.8077171   .6696173    -1.21   0.249    -2.254337    .6389031
       lnquotas_yr1858 |  -1.641911   .6282684    -2.61   0.021    -2.999203   -.2846202
       lnquotas_yr1859 |  -1.948403   .6764665    -2.88   0.013     -3.40982   -.4869865
       lnquotas_yr1860 |  -1.711871   1.159979    -1.48   0.164    -4.217854    .7941126
       lnquotas_yr1861 |   -.980043   .8377909    -1.17   0.263     -2.78998    .8298942
       lnquotas_yr1862 |  -1.328779   .9125339    -1.46   0.169    -3.300188     .642631
       lnquotas_yr1863 |  -1.360875   .6155556    -2.21   0.046    -2.690701   -.0310476
       lnquotas_yr1864 |  -2.015935   .9771993    -2.06   0.060    -4.127046    .0951755
         route1_yr1850 |   .2841424   .4057559     0.70   0.496    -.5924398    1.160725
         route1_yr1851 |   .1025895   .4009189     0.26   0.802    -.7635431     .968722
         route1_yr1852 |   .7722147   .5140857     1.50   0.157    -.3383999    1.882829
         route1_yr1854 |  -.3341441   .3923627    -0.85   0.410    -1.181792    .5135039
         route1_yr1855 |   .5115154   .3843427     1.33   0.206    -.3188064    1.341837
         route1_yr1856 |  -.5673991    .655469    -0.87   0.402    -1.983454    .8486556
         route1_yr1857 |  -.2955998   .2670476    -1.11   0.288    -.8725211    .2813214
         route1_yr1858 |  -.1990746   .2846228    -0.70   0.497    -.8139648    .4158155
         route1_yr1859 |   .3322849   .1616491     2.06   0.060    -.0169368    .6815066
         route1_yr1860 |   .3051365   .6028094     0.51   0.621     -.997154    1.607427
         route1_yr1861 |   .2539827   .4366396     0.58   0.571    -.6893198    1.197285
         route1_yr1862 |   .2584161   .3304543     0.78   0.448    -.4554871    .9723193
         route1_yr1863 |   .2899107   .2510589     1.15   0.269    -.2524692    .8322905
         route1_yr1864 |   .5595804   .5888002     0.95   0.359    -.7124451    1.831606
   dist_nanjing_yr1850 |  -.3676349   .3278606    -1.12   0.282    -1.075935    .3406649
   dist_nanjing_yr1851 |  -.2492113   .3498147    -0.71   0.489     -1.00494    .5065174
   dist_nanjing_yr1852 |   .0884845   .2194586     0.40   0.693    -.3856269    .5625959
   dist_nanjing_yr1854 |  -.0457125   .4954372    -0.09   0.928    -1.116039    1.024614
   dist_nanjing_yr1855 |  -.6173668   .7157783    -0.86   0.404    -2.163712    .9289782
   dist_nanjing_yr1856 |  -1.581056   .6674781    -2.37   0.034    -3.023055   -.1390573
   dist_nanjing_yr1857 |   -1.40395   .5498374    -2.55   0.024    -2.591801   -.2160981
   dist_nanjing_yr1858 |  -.7016179   .4847747    -1.45   0.171     -1.74891    .3456741
   dist_nanjing_yr1859 |  -1.245254   .3981473    -3.13   0.008    -2.105398   -.3851086
   dist_nanjing_yr1860 |  -.5093719   .5711816    -0.89   0.389    -1.743335     .724591
   dist_nanjing_yr1861 |  -1.255988   .5660826    -2.22   0.045    -2.478935   -.0330407
   dist_nanjing_yr1862 |  -1.070464   .4238246    -2.53   0.025    -1.986081   -.1548464
   dist_nanjing_yr1863 |  -1.440982   .4647935    -3.10   0.008    -2.445108    -.436857
   dist_nanjing_yr1864 |  -1.032386   .4903653    -2.11   0.055    -2.091756    .0269837
        mainriv_yr1850 |  -.3401387   .3119722    -1.09   0.295    -1.014114    .3338362
        mainriv_yr1851 |  -.3197588    .300087    -1.07   0.306    -.9680573    .3285397
        mainriv_yr1852 |  -.1534287   .3304575    -0.46   0.650    -.8673388    .5604814
        mainriv_yr1854 |  -.9313396   .8129418    -1.15   0.273    -2.687594    .8249144
        mainriv_yr1855 |  -1.574775   .4524968    -3.48   0.004    -2.552335    -.597215
        mainriv_yr1856 |  -1.576604   .4757216    -3.31   0.006    -2.604338   -.5488697
        mainriv_yr1857 |  -1.101868   .4828452    -2.28   0.040    -2.144992   -.0587443
        mainriv_yr1858 |  -.4499467   .4106035    -1.10   0.293    -1.337002    .4371083
        mainriv_yr1859 |  -1.079881   .4181124    -2.58   0.023    -1.983158   -.1766038
        mainriv_yr1860 |  -1.100241   .5299456    -2.08   0.058    -2.245118    .0446371
        mainriv_yr1861 |  -.5837191   .4484809    -1.30   0.216    -1.552603    .3851649
        mainriv_yr1862 |  -.1186846   .4534629    -0.26   0.798    -1.098332    .8609625
        mainriv_yr1863 |  -.5945447   .3173719    -1.87   0.084    -1.280185    .0910955
        mainriv_yr1864 |  -.3844466   .4178545    -0.92   0.374    -1.287166    .5182732
        lnwheat_yr1850 |   3.421115   3.675326     0.93   0.369    -4.518943    11.36117
        lnwheat_yr1851 |   1.887374   2.883222     0.65   0.524    -4.341448    8.116196
        lnwheat_yr1852 |  -2.435695    4.48763    -0.54   0.596    -12.13063    7.259239
        lnwheat_yr1854 |   8.968768   3.693266     2.43   0.030     .9899516    16.94758
        lnwheat_yr1855 |   8.365916   5.744712     1.46   0.169    -4.044781    20.77661
        lnwheat_yr1856 |   12.02198   2.178821     5.52   0.000      7.31492    16.72903
        lnwheat_yr1857 |   6.844283   4.506922     1.52   0.153    -2.892329     16.5809
        lnwheat_yr1858 |   6.608124   2.872465     2.30   0.039     .4025412    12.81371
        lnwheat_yr1859 |  -1.517195   3.753834    -0.40   0.693    -9.626861    6.592471
        lnwheat_yr1860 |   1.792874   2.238021     0.80   0.437    -3.042076    6.627824
        lnwheat_yr1861 |   .3542121    3.69028     0.10   0.925    -7.618153    8.326577
        lnwheat_yr1862 |   2.246823   4.145667     0.54   0.597    -6.709346    11.20299
        lnwheat_yr1863 |   8.567113   3.836535     2.23   0.044     .2787826    16.85544
        lnwheat_yr1864 |   5.521751   2.134097     2.59   0.023     .9113141    10.13219
         lnrice_yr1850 |  -3.220573   1.678437    -1.92   0.077    -6.846615    .4054691
         lnrice_yr1851 |  -2.825276   1.935396    -1.46   0.168    -7.006444    1.355892
         lnrice_yr1852 |   .1693784   2.329471     0.07   0.943    -4.863139    5.201895
         lnrice_yr1854 |  -2.324597   2.816588    -0.83   0.424    -8.409466    3.760271
         lnrice_yr1855 |  -5.731384   4.510155    -1.27   0.226    -15.47498    4.012213
         lnrice_yr1856 |  -6.376208   3.002229    -2.12   0.053    -12.86213    .1097136
         lnrice_yr1857 |  -6.392285   2.111906    -3.03   0.010    -10.95478   -1.829789
         lnrice_yr1858 |  -4.186587   2.175723    -1.92   0.076     -8.88695    .5137772
         lnrice_yr1859 |  -6.190019   2.782194    -2.22   0.044    -12.20058   -.1794536
         lnrice_yr1860 |  -5.492098   3.068347    -1.79   0.097    -12.12086    1.136662
         lnrice_yr1861 |  -1.466991   2.839108    -0.52   0.614    -7.600512     4.66653
         lnrice_yr1862 |  -5.285686   2.893516    -1.83   0.091    -11.53675    .9653748
         lnrice_yr1863 |   -7.35537   2.608916    -2.82   0.014    -12.99159   -1.719149
         lnrice_yr1864 |  -6.302169   2.374204    -2.65   0.020    -11.43132   -1.173013
          lnpop_yr1850 |   .0634428   .4772302     0.13   0.896    -.9675505    1.094436
          lnpop_yr1851 |   .2134215   .5242413     0.41   0.691    -.9191329    1.345976
          lnpop_yr1852 |  -.2083153   .6192659    -0.34   0.742    -1.546158    1.129527
          lnpop_yr1854 |   .2798758   .6456797     0.43   0.672     -1.11503    1.674782
          lnpop_yr1855 |   .5273247   .9447901     0.56   0.586     -1.51377     2.56842
          lnpop_yr1856 |  -.3348654    .754646    -0.44   0.665    -1.965179    1.295448
          lnpop_yr1857 |  -.3202808   .6077645    -0.53   0.607    -1.633276    .9927147
          lnpop_yr1858 |   .0027693   .6210521     0.00   0.997    -1.338932    1.344471
          lnpop_yr1859 |   .5431308   .6640358     0.82   0.428    -.8914314    1.977693
          lnpop_yr1860 |   .2018318    .820236     0.25   0.809     -1.57018    1.973844
          lnpop_yr1861 |  -1.064559   .7699723    -1.38   0.190    -2.727983    .5988651
          lnpop_yr1862 |  -.8216607   .8006689    -1.03   0.324    -2.551401    .9080793
          lnpop_yr1863 |    .280718   .5455601     0.51   0.616    -.8978929    1.459329
          lnpop_yr1864 |   -.144474   .5119773    -0.28   0.782    -1.250534    .9615857
         lnarea_yr1850 |  -.1078162   .3556998    -0.30   0.767    -.8762588    .6606265
         lnarea_yr1851 |  -.2427941   .2619967    -0.93   0.371    -.8088037    .3232154
         lnarea_yr1852 |  -.3227509   .4060382    -0.79   0.441    -1.199943    .5544413
         lnarea_yr1854 |   .0986222   .7094511     0.14   0.892    -1.434054    1.631298
         lnarea_yr1855 |   .1807698   .5870309     0.31   0.763    -1.087433    1.448973
         lnarea_yr1856 |   .4305027   .5715522     0.75   0.465    -.8042608    1.665266
         lnarea_yr1857 |   .7756291    .683786     1.13   0.277    -.7016007    2.252859
         lnarea_yr1858 |   .5164576   .5876315     0.88   0.395     -.753043    1.785958
         lnarea_yr1859 |   .1744913   .5475084     0.32   0.755    -1.008329    1.357311
         lnarea_yr1860 |   .4495623   .5645563     0.80   0.440    -.7700875    1.669212
         lnarea_yr1861 |    1.47473   .8369481     1.76   0.102    -.3333864    3.282847
         lnarea_yr1862 |   1.205216   .7252671     1.66   0.120    -.3616283     2.77206
         lnarea_yr1863 |   1.252043   .6173753     2.03   0.064    -.0817154    2.585801
         lnarea_yr1864 |    1.05395   .5377066     1.96   0.072    -.1076948    2.215594
Zeng_all0_invdist_Post |   .2690699   .0638436     4.21   0.001     .1311442    .4069956
                 _cons |   3.173194   9.483344     0.33   0.743    -17.31433    23.66071
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_invdist_Post)  se  bdec(3) rdec(3) nocons app
> end
Results\\Appendix_Table_B1_IV.doc
dir : seeout

. 
. 
. 
. *** *** *** *** *** ***
.  *** *** *** *** *** ***
. xi: reghdfe  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_
> Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post       Zeng_all0_invdist_pc
> _Post, absorb(year cntyid  prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     13) =    1122.57
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6697
                                                  Adj R-squared   =     0.5410
Number of clusters (prefid)  =         14         Within R-sq.    =     0.1153
Number of clusters (cntyid)  =         74         Root MSE        =     1.1188

                                      (Std. Err. adjusted for 14 clusters in prefid cntyid)
-------------------------------------------------------------------------------------------
                          |               Robust
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
             capital_Post |  -1.409048   .3533852    -3.99   0.002     -2.17249   -.6456059
          lnurbanpop_Post |   .4734599   .1242811     3.81   0.002      .204967    .7419528
            lnjinshi_Post |   .4051828   .1346106     3.01   0.010     .1143744    .6959913
            lnquotas_Post |  -1.175197   .4301312    -2.73   0.017    -2.104439   -.2459546
              route1_Post |  -.0724558    .126861    -0.57   0.578    -.3465224    .2016108
        dist_nanjing_Post |  -.9831852   .8123639    -1.21   0.248    -2.738191    .7718203
             mainriv_Post |  -.6709632   .2200901    -3.05   0.009    -1.146439   -.1954874
          dist2canal_Post |   .1890491   .9433238     0.20   0.844    -1.848878    2.226976
             lnwheat_Post |   5.687431   1.803521     3.15   0.008     1.791161      9.5837
              lnrice_Post |  -3.045239   .7155142    -4.26   0.001    -4.591013   -1.499464
               lnpop_Post |   .2015364    .374537     0.54   0.600    -.6076016    1.010674
              lnarea_Post |   .7573249   .3688396     2.05   0.061    -.0395047    1.554154
Zeng_all0_invdist_pc_Post |   .0878667   .0206339     4.26   0.001     .0432898    .1324436
                    _cons |  -3.915651   4.598546    -0.85   0.410    -13.85021    6.018904
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons
>  append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

.  
. 
.  
. xi: reghdfe  lnmartyr1  capital_yr* lnurbanpop_yr*  lnjinshi_yr*  lnquotas_yr* route1_yr*  dist_nanjing_yr* m
> ainriv_yr*    lnwheat_yr* lnrice_yr* lnpop_yr* lnarea_yr*    Zeng_all0_invdist_pc_Post, absorb(year cntyid  p
> refidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F( 155,     13) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7376
                                                  Adj R-squared   =     0.5564
Number of clusters (prefid)  =         14         Within R-sq.    =     0.2972
Number of clusters (cntyid)  =         74         Root MSE        =     1.0998

                                      (Std. Err. adjusted for 14 clusters in prefid cntyid)
-------------------------------------------------------------------------------------------
                          |               Robust
                lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
           capital_yr1850 |  -.4097634   .5133972    -0.80   0.439    -1.518891    .6993638
           capital_yr1851 |  -.2484227   .4727179    -0.53   0.608    -1.269668    .7728223
           capital_yr1852 |   .1947214   .9575611     0.20   0.842    -1.873964    2.263406
           capital_yr1854 |  -.2239518   1.071942    -0.21   0.838    -2.539742    2.091838
           capital_yr1855 |  -.8734552   .6293054    -1.39   0.188    -2.232987    .4860764
           capital_yr1856 |  -1.077547   .5337443    -2.02   0.065    -2.230632     .075537
           capital_yr1857 |  -1.455525     .57956    -2.51   0.026    -2.707588   -.2034612
           capital_yr1858 |  -1.480555   .4944192    -2.99   0.010    -2.548683   -.4124273
           capital_yr1859 |  -1.618753   .5288364    -3.06   0.009    -2.761235   -.4762714
           capital_yr1860 |  -1.700059    .430265    -3.95   0.002     -2.62959   -.7705281
           capital_yr1861 |  -2.763821   .9863012    -2.80   0.015    -4.894595   -.6330464
           capital_yr1862 |   -1.61592   .6167198    -2.62   0.021    -2.948262   -.2835784
           capital_yr1863 |  -2.262584   .5811014    -3.89   0.002    -3.517977   -1.007191
           capital_yr1864 |  -1.927234   .4489465    -4.29   0.001    -2.897124   -.9573438
        lnurbanpop_yr1850 |   .2791304   .2397642     1.16   0.265    -.2388486    .7971094
        lnurbanpop_yr1851 |   .1683175   .2015215     0.84   0.419    -.2670432    .6036781
        lnurbanpop_yr1852 |   .2127898   .3775463     0.56   0.583    -.6028493    1.028429
        lnurbanpop_yr1854 |     .22445   .3017802     0.74   0.470    -.4275064    .8764064
        lnurbanpop_yr1855 |   .4478185   .2895763     1.55   0.146     -.177773     1.07341
        lnurbanpop_yr1856 |   .2463369   .3113063     0.79   0.443    -.4261994    .9188732
        lnurbanpop_yr1857 |   .5531974   .1668976     3.31   0.006      .192637    .9137578
        lnurbanpop_yr1858 |   .6595069   .1921049     3.43   0.004     .2444895    1.074524
        lnurbanpop_yr1859 |   .5973929     .19279     3.10   0.008     .1808954     1.01389
        lnurbanpop_yr1860 |   .6845973   .2217385     3.09   0.009     .2055603    1.163634
        lnurbanpop_yr1861 |   1.038819   .3756143     2.77   0.016     .2273539    1.850285
        lnurbanpop_yr1862 |   .7528709   .2412331     3.12   0.008     .2317185    1.274023
        lnurbanpop_yr1863 |   .9489378   .3220811     2.95   0.011     .2531239    1.644752
        lnurbanpop_yr1864 |   .9546943   .2195313     4.35   0.001     .4804257    1.428963
          lnjinshi_yr1850 |   .2607736   .3239514     0.80   0.435    -.4390808    .9606279
          lnjinshi_yr1851 |   .1771224    .311886     0.57   0.580    -.4966664    .8509112
          lnjinshi_yr1852 |  -.3651996   .2547664    -1.43   0.175     -.915589    .1851897
          lnjinshi_yr1854 |   .1633587   .1615157     1.01   0.330    -.1855749    .5122922
          lnjinshi_yr1855 |   .1495981    .244072     0.61   0.550    -.3776874    .6768835
          lnjinshi_yr1856 |   .4368153   .2922749     1.49   0.159    -.1946064    1.068237
          lnjinshi_yr1857 |   .6198951   .3241605     1.91   0.078    -.0804111    1.320201
          lnjinshi_yr1858 |   .3034767   .2212497     1.37   0.193    -.1745042    .7814576
          lnjinshi_yr1859 |    .338273   .2525876     1.34   0.203    -.2074093    .8839552
          lnjinshi_yr1860 |   .3783222   .2399137     1.58   0.139    -.1399798    .8966242
          lnjinshi_yr1861 |   .5698204   .3666338     1.55   0.144    -.2222438    1.361885
          lnjinshi_yr1862 |   .5626735   .4421521     1.27   0.225    -.3925381    1.517885
          lnjinshi_yr1863 |   .5537926   .3633877     1.52   0.151    -.2312588    1.338844
          lnjinshi_yr1864 |    .586328   .3940051     1.49   0.161    -.2648682    1.437524
          lnquotas_yr1850 |   -.603631    .616995    -0.98   0.346    -1.936568    .7293056
          lnquotas_yr1851 |  -.7137533   .5453759    -1.31   0.213    -1.891966    .4644597
          lnquotas_yr1852 |    .043315    .710833     0.06   0.952    -1.492346    1.578976
          lnquotas_yr1854 |   .0345771   .9131091     0.04   0.970    -1.938075    2.007229
          lnquotas_yr1855 |   -1.77061   .6752215    -2.62   0.021    -3.229338   -.3118832
          lnquotas_yr1856 |  -.8090527   1.406886    -0.58   0.575    -3.848445    2.230339
          lnquotas_yr1857 |  -1.081224   .5971442    -1.81   0.093    -2.371276    .2088274
          lnquotas_yr1858 |  -1.915419   .5899812    -3.25   0.006    -3.189995   -.6408418
          lnquotas_yr1859 |  -2.221911   .6840221    -3.25   0.006     -3.69965   -.7441708
          lnquotas_yr1860 |  -1.985378   1.109785    -1.79   0.097    -4.382923    .4121674
          lnquotas_yr1861 |   -1.25355   .8077785    -1.55   0.145     -2.99865    .4915492
          lnquotas_yr1862 |  -1.602286   .9199786    -1.74   0.105    -3.589779    .3852073
          lnquotas_yr1863 |  -1.634382   .5207416    -3.14   0.008    -2.759375   -.5093879
          lnquotas_yr1864 |  -2.289442   .9178686    -2.49   0.027    -4.272377   -.3065077
            route1_yr1850 |   .2841424   .4057559     0.70   0.496    -.5924398    1.160725
            route1_yr1851 |   .1025895   .4009189     0.26   0.802    -.7635431     .968722
            route1_yr1852 |   .7722147   .5140857     1.50   0.157    -.3383999    1.882829
            route1_yr1854 |  -.2164957   .5243841    -0.41   0.686    -1.349359    .9163672
            route1_yr1855 |   .6291639   .3975307     1.58   0.138     -.229649    1.487977
            route1_yr1856 |  -.4497507   .7914152    -0.57   0.580    -2.159499    1.259998
            route1_yr1857 |  -.1779514   .3737444    -0.48   0.642    -.9853772    .6294743
            route1_yr1858 |  -.0814262   .4217117    -0.19   0.850    -.9924789    .8296264
            route1_yr1859 |   .4499333   .1820054     2.47   0.028     .0567346     .843132
            route1_yr1860 |   .4227849   .7291512     0.58   0.572    -1.152451     1.99802
            route1_yr1861 |   .3716311   .5739903     0.65   0.529    -.8683996    1.611662
            route1_yr1862 |   .3760645   .4155565     0.90   0.382    -.5216908     1.27382
            route1_yr1863 |   .4075591    .331142     1.23   0.240    -.3078296    1.122948
            route1_yr1864 |   .6772288   .7065592     0.96   0.355    -.8491996    2.203657
      dist_nanjing_yr1850 |  -.3676349   .3278606    -1.12   0.282    -1.075935    .3406649
      dist_nanjing_yr1851 |  -.2492113   .3498147    -0.71   0.489     -1.00494    .5065174
      dist_nanjing_yr1852 |   .0884845   .2194586     0.40   0.693    -.3856269    .5625959
      dist_nanjing_yr1854 |   .0128477   .4652341     0.03   0.978    -.9922294    1.017925
      dist_nanjing_yr1855 |  -.5588066   .6974792    -0.80   0.437    -2.065619    .9480057
      dist_nanjing_yr1856 |  -1.522496   .6555218    -2.32   0.037    -2.938665   -.1063271
      dist_nanjing_yr1857 |  -1.345389   .5427756    -2.48   0.028    -2.517985   -.1727938
      dist_nanjing_yr1858 |  -.6430577   .4664462    -1.38   0.191    -1.650753    .3646381
      dist_nanjing_yr1859 |  -1.186693   .4004708    -2.96   0.011    -2.051858   -.3215288
      dist_nanjing_yr1860 |  -.4508117   .5384649    -0.84   0.418    -1.614094    .7124711
      dist_nanjing_yr1861 |  -1.197428   .5528238    -2.17   0.049    -2.391731   -.0031243
      dist_nanjing_yr1862 |  -1.011904   .3769894    -2.68   0.019     -1.82634   -.1974674
      dist_nanjing_yr1863 |  -1.382422   .4375493    -3.16   0.008     -2.32769   -.4371543
      dist_nanjing_yr1864 |  -.9738259   .4508036    -2.16   0.050    -1.947728     .000076
           mainriv_yr1850 |  -.3401387   .3119722    -1.09   0.295    -1.014114    .3338362
           mainriv_yr1851 |  -.3197588    .300087    -1.07   0.306    -.9680573    .3285397
           mainriv_yr1852 |  -.1534287   .3304575    -0.46   0.650    -.8673388    .5604814
           mainriv_yr1854 |  -.9245436   .7760365    -1.19   0.255    -2.601069    .7519813
           mainriv_yr1855 |  -1.567979   .4712698    -3.33   0.005    -2.586095   -.5498626
           mainriv_yr1856 |  -1.569808   .4763197    -3.30   0.006    -2.598834   -.5407815
           mainriv_yr1857 |  -1.095072   .4758355    -2.30   0.039    -2.123052   -.0670917
           mainriv_yr1858 |  -.4431507   .4101063    -1.08   0.300    -1.329132    .4428301
           mainriv_yr1859 |  -1.073085   .4495592    -2.39   0.033    -2.044298   -.1018712
           mainriv_yr1860 |  -1.093445   .5112987    -2.14   0.052    -2.198038    .0111491
           mainriv_yr1861 |  -.5769232   .4714406    -1.22   0.243    -1.595409    .4415624
           mainriv_yr1862 |  -.1118886   .4615793    -0.24   0.812     -1.10907    .8852928
           mainriv_yr1863 |  -.5877487   .3039463    -1.93   0.075    -1.244385    .0688873
           mainriv_yr1864 |  -.3776506   .3892331    -0.97   0.350    -1.218538    .4632364
           lnwheat_yr1850 |   3.421115   3.675326     0.93   0.369    -4.518943    11.36117
           lnwheat_yr1851 |   1.887374   2.883222     0.65   0.524    -4.341448    8.116196
           lnwheat_yr1852 |  -2.435695    4.48763    -0.54   0.596    -12.13063    7.259239
           lnwheat_yr1854 |   9.849723     3.9535     2.49   0.027     1.308706    18.39074
           lnwheat_yr1855 |   9.246871   5.645581     1.64   0.125    -2.949665    21.44341
           lnwheat_yr1856 |   12.90293   2.405965     5.36   0.000     7.705161     18.1007
           lnwheat_yr1857 |   7.725239   4.124078     1.87   0.084    -1.184291    16.63477
           lnwheat_yr1858 |    7.48908   2.780074     2.69   0.018     1.483095    13.49506
           lnwheat_yr1859 |  -.6362396   3.783193    -0.17   0.869    -8.809331    7.536852
           lnwheat_yr1860 |    2.67383   2.175223     1.23   0.241    -2.025453    7.373112
           lnwheat_yr1861 |   1.235167   3.698361     0.33   0.744    -6.754656    9.224991
           lnwheat_yr1862 |   3.127779   3.971753     0.79   0.445    -5.452672    11.70823
           lnwheat_yr1863 |   9.448068   3.672903     2.57   0.023     1.513243    17.38289
           lnwheat_yr1864 |   6.402706   2.200808     2.91   0.012      1.64815    11.15726
            lnrice_yr1850 |  -3.220573   1.678437    -1.92   0.077    -6.846615    .4054691
            lnrice_yr1851 |  -2.825276   1.935396    -1.46   0.168    -7.006444    1.355892
            lnrice_yr1852 |   .1693784   2.329471     0.07   0.943    -4.863139    5.201895
            lnrice_yr1854 |  -1.527687   2.441548    -0.63   0.542     -6.80233    3.746956
            lnrice_yr1855 |  -4.934474   4.476046    -1.10   0.290    -14.60438    4.735436
            lnrice_yr1856 |  -5.579298   3.142984    -1.78   0.099     -12.3693    1.210706
            lnrice_yr1857 |  -5.595375   2.153547    -2.60   0.022    -10.24783   -.9429201
            lnrice_yr1858 |  -3.389677   2.079106    -1.63   0.127    -7.881311    1.101958
            lnrice_yr1859 |  -5.393109   3.152689    -1.71   0.111    -12.20408    1.417861
            lnrice_yr1860 |  -4.695188   2.801277    -1.68   0.118    -10.74698    1.356603
            lnrice_yr1861 |  -.6700809   2.711239    -0.25   0.809    -6.527357    5.187195
            lnrice_yr1862 |  -4.488776   2.308493    -1.94   0.074    -9.475972    .4984193
            lnrice_yr1863 |   -6.55846   2.223441    -2.95   0.011    -11.36191   -1.755007
            lnrice_yr1864 |  -5.505259   1.954448    -2.82   0.015    -9.727588    -1.28293
             lnpop_yr1850 |   .0634428   .4772302     0.13   0.896    -.9675505    1.094436
             lnpop_yr1851 |   .2134215   .5242413     0.41   0.691    -.9191329    1.345976
             lnpop_yr1852 |  -.2083153   .6192659    -0.34   0.742    -1.546158    1.129527
             lnpop_yr1854 |    .577749   .5851736     0.99   0.342    -.6864418     1.84194
             lnpop_yr1855 |   .8251979   .9454879     0.87   0.399    -1.217405      2.8678
             lnpop_yr1856 |  -.0369921   .7951165    -0.05   0.964    -1.754737    1.680753
             lnpop_yr1857 |  -.0224075   .6112378    -0.04   0.971    -1.342907    1.298091
             lnpop_yr1858 |   .3006425   .6255736     0.48   0.639    -1.050827    1.652112
             lnpop_yr1859 |   .8410041   .7288418     1.15   0.269    -.7335628    2.415571
             lnpop_yr1860 |    .499705   .7322789     0.68   0.507    -1.082287    2.081697
             lnpop_yr1861 |  -.7666856   .7126399    -1.08   0.302    -2.306251    .7728793
             lnpop_yr1862 |  -.5237874   .7632121    -0.69   0.505    -2.172607    1.125032
             lnpop_yr1863 |   .5785912   .4423891     1.31   0.214    -.3771323    1.534315
             lnpop_yr1864 |   .1533993   .4371539     0.35   0.731    -.7910142    1.097813
            lnarea_yr1850 |  -.1078162   .3556998    -0.30   0.767    -.8762588    .6606265
            lnarea_yr1851 |  -.2427941   .2619967    -0.93   0.371    -.8088037    .3232154
            lnarea_yr1852 |  -.3227509   .4060382    -0.79   0.441    -1.199943    .5544413
            lnarea_yr1854 |   4.10e-06    .714383     0.00   1.000    -1.543327    1.543335
            lnarea_yr1855 |   .0821517   .5706667     0.14   0.888    -1.150699    1.315002
            lnarea_yr1856 |   .3318846   .5710898     0.58   0.571    -.9018801    1.565649
            lnarea_yr1857 |   .6770109   .6705358     1.01   0.331    -.7715935    2.125615
            lnarea_yr1858 |   .4178395   .5597672     0.75   0.469    -.7914641    1.627143
            lnarea_yr1859 |   .0758732   .5465661     0.14   0.892    -1.104911    1.256657
            lnarea_yr1860 |   .3509442   .5382558     0.65   0.526    -.8118868    1.513775
            lnarea_yr1861 |   1.376112   .8405172     1.64   0.126    -.4397151    3.191939
            lnarea_yr1862 |   1.106598   .6989926     1.58   0.137     -.403484     2.61668
            lnarea_yr1863 |   1.153425   .5894046     1.96   0.072    -.1199063    2.426756
            lnarea_yr1864 |   .9553314   .4928349     1.94   0.075    -.1093737    2.020037
Zeng_all0_invdist_pc_Post |   .0891295   .0185318     4.81   0.000     .0490939     .129165
                    _cons |  -.9985521   8.837167    -0.11   0.912    -20.09009    18.09299
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_invdist_pc_Post )  se  bdec(3) rdec(3) nocons
>  append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

.  
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** 
.  
. xi: reghdfe  lnmartyr1    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjin
> g_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post      Zeng_all0_Post , ab
> sorb(year cntyid prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     13) =    6671.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6684
                                                  Adj R-squared   =     0.5391
Number of clusters (prefid)  =         14         Within R-sq.    =     0.1117
Number of clusters (cntyid)  =         74         Root MSE        =     1.1211

                              (Std. Err. adjusted for 14 clusters in prefid cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -1.388029   .4208143    -3.30   0.006    -2.297143   -.4789148
  lnurbanpop_Post |   .4065032   .1150657     3.53   0.004     .1579189    .6550876
    lnjinshi_Post |   .3379932   .1393711     2.43   0.031     .0369002    .6390863
    lnquotas_Post |  -.9130775   .4076025    -2.24   0.043    -1.793649   -.0325058
      route1_Post |  -.1637036   .0748202    -2.19   0.048    -.3253429   -.0020643
dist_nanjing_Post |  -1.558502   .8086435    -1.93   0.076     -3.30547    .1884658
     mainriv_Post |  -.7032625   .2380895    -2.95   0.011    -1.217624   -.1889013
  dist2canal_Post |   .7314203    .914327     0.80   0.438    -1.243863    2.706704
     lnwheat_Post |   4.948021   2.099402     2.36   0.035     .4125391    9.483503
      lnrice_Post |  -3.901067   1.127393    -3.46   0.004    -6.336651   -1.465483
       lnpop_Post |  -.0518096   .3880317    -0.13   0.896    -.8901011    .7864819
      lnarea_Post |    .905266   .3803058     2.38   0.033     .0836653    1.726867
   Zeng_all0_Post |   .1818727   .0465233     3.91   0.002     .0813653    .2823802
            _cons |   -.399339   5.001319    -0.08   0.938    -11.20403    10.40535
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

. 
. **** *** *** 
. 
. 
. **** *** *** 
. xi: reghdfe  lnmartyr1    capital_yr* lnurbanpop_yr*  lnjinshi_yr*  lnquotas_yr* route1_yr*  dist_nanjing_yr*
>  mainriv_yr* dist2canal_yr*  lnwheat_yr* lnrice_yr* lnpop_yr* lnarea_yr*   Zeng_all0_Post , absorb(year cntyi
> d  prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F( 169,     13) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7430
                                                  Adj R-squared   =     0.5560
Number of clusters (prefid)  =         14         Within R-sq.    =     0.3115
Number of clusters (cntyid)  =         74         Root MSE        =     1.1003

                                (Std. Err. adjusted for 14 clusters in prefid cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
          lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
     capital_yr1850 |  -.3829612   .4996723    -0.77   0.457    -1.462438    .6965152
     capital_yr1851 |    -.23679   .4688275    -0.51   0.622     -1.24963    .7760503
     capital_yr1852 |   .2435674   .9598545     0.25   0.804    -1.830072    2.317207
     capital_yr1854 |  -.2040176   1.189548    -0.17   0.866    -2.773881    2.365845
     capital_yr1855 |  -.9390106    .588005    -1.60   0.134    -2.209318    .3312969
     capital_yr1856 |  -1.143674   .5307065    -2.16   0.050    -2.290196    .0028472
     capital_yr1857 |  -1.420302   .5656971    -2.51   0.026    -2.642416   -.1981876
     capital_yr1858 |  -1.409464   .4314887    -3.27   0.006    -2.341639   -.4772897
     capital_yr1859 |  -1.533902   .5503305    -2.79   0.015    -2.722819   -.3449848
     capital_yr1860 |  -1.672215   .5110729    -3.27   0.006    -2.776321   -.5681095
     capital_yr1861 |   -2.58582   .7932611    -3.26   0.006    -4.299557   -.8720841
     capital_yr1862 |   -1.46642   .5670357    -2.59   0.023    -2.691426   -.2414142
     capital_yr1863 |  -2.124917   .4698847    -4.52   0.001    -3.140041   -1.109793
     capital_yr1864 |  -1.803079    .427048    -4.22   0.001     -2.72566   -.8804978
  lnurbanpop_yr1850 |   .2577839   .2527556     1.02   0.326    -.2882614    .8038292
  lnurbanpop_yr1851 |   .1590526   .2211316     0.72   0.485    -.3186732    .6367784
  lnurbanpop_yr1852 |   .1738866   .3980696     0.44   0.669    -.6860905    1.033864
  lnurbanpop_yr1854 |   .1669546   .3062499     0.55   0.595    -.4946581    .8285673
  lnurbanpop_yr1855 |   .4584109   .2657885     1.72   0.108    -.1157902    1.032612
  lnurbanpop_yr1856 |   .2573846   .3435851     0.75   0.467    -.4848858     .999655
  lnurbanpop_yr1857 |   .4835256   .1928293     2.51   0.026     .0669432    .9001079
  lnurbanpop_yr1858 |   .5612682   .1956586     2.87   0.013     .1385736    .9839628
  lnurbanpop_yr1859 |   .4881945   .1886405     2.59   0.023     .0806615    .8957275
  lnurbanpop_yr1860 |   .6208024   .2176385     2.85   0.014      .150623    1.090982
  lnurbanpop_yr1861 |    .855433   .3563905     2.40   0.032     .0854981    1.625368
  lnurbanpop_yr1862 |   .5921833   .2459277     2.41   0.032     .0608888    1.123478
  lnurbanpop_yr1863 |   .7976746   .3163074     2.52   0.026      .114334    1.481015
  lnurbanpop_yr1864 |   .8141928   .2022722     4.03   0.001     .3772102    1.251175
    lnjinshi_yr1850 |   .2537009   .3406942     0.74   0.470    -.4823241    .9897258
    lnjinshi_yr1851 |   .1740527   .3249082     0.54   0.601    -.5278688    .8759742
    lnjinshi_yr1852 |  -.3780894   .2750829    -1.37   0.193    -.9723699    .2161911
    lnjinshi_yr1854 |    .101368   .1596345     0.64   0.536    -.2435013    .4462373
    lnjinshi_yr1855 |   .1101669   .2409372     0.46   0.655    -.4103461      .63068
    lnjinshi_yr1856 |    .397535   .2865861     1.39   0.189    -.2215966    1.016667
    lnjinshi_yr1857 |     .55387   .3689153     1.50   0.157    -.2431231    1.350863
    lnjinshi_yr1858 |   .2279866   .2641003     0.86   0.404    -.3425676    .7985407
    lnjinshi_yr1859 |   .2591516   .2264921     1.14   0.273    -.2301548     .748458
    lnjinshi_yr1860 |   .3142444   .2254257     1.39   0.187    -.1727581     .801247
    lnjinshi_yr1861 |   .4661183   .3773297     1.24   0.239    -.3490529    1.281289
    lnjinshi_yr1862 |   .4664921   .4972081     0.94   0.365    -.6076606    1.540645
    lnjinshi_yr1863 |   .4607339   .4320296     1.07   0.306    -.4726093    1.394077
    lnjinshi_yr1864 |    .496835   .4436673     1.12   0.283    -.4616499     1.45532
    lnquotas_yr1850 |   -.579331   .6705271    -0.86   0.403    -2.027917    .8692548
    lnquotas_yr1851 |  -.7032065   .5849721    -1.20   0.251    -1.966962    .5605489
    lnquotas_yr1852 |   .0876009   .7615393     0.12   0.910    -1.557605    1.732807
    lnquotas_yr1854 |   .2860319   .9988434     0.29   0.779    -1.871838    2.443902
    lnquotas_yr1855 |  -1.596664   .6372727    -2.51   0.026    -2.973408   -.2199202
    lnquotas_yr1856 |  -.6356248   1.494836    -0.43   0.678    -3.865022    2.593772
    lnquotas_yr1857 |  -.8159083    .700705    -1.16   0.265    -2.329689    .6978727
    lnquotas_yr1858 |  -1.617583   .6373835    -2.54   0.025    -2.994567   -.2405999
    lnquotas_yr1859 |  -1.911599   .7016756    -2.72   0.017    -3.427477   -.3957211
    lnquotas_yr1860 |  -1.726752   1.168432    -1.48   0.163    -4.250996    .7974921
    lnquotas_yr1861 |  -.8587859    .872956    -0.98   0.343    -2.744693    1.027121
    lnquotas_yr1862 |  -1.233361   .9302029    -1.33   0.208    -3.242942    .7762203
    lnquotas_yr1863 |  -1.276185   .6560342    -1.95   0.074    -2.693461    .1410905
    lnquotas_yr1864 |  -1.943497   1.011941    -1.92   0.077    -4.129662    .2426687
      route1_yr1850 |   .2883738   .4176516     0.69   0.502    -.6139076    1.190655
      route1_yr1851 |    .104426   .4119877     0.25   0.804    -.7856194    .9944714
      route1_yr1852 |   .7799262   .5456218     1.43   0.176    -.3988181    1.958671
      route1_yr1854 |  -.3128447   .4084719    -0.77   0.457    -1.195295    .5696052
      route1_yr1855 |   .5193183   .4391195     1.18   0.258    -.4293417    1.467978
      route1_yr1856 |  -.5596865    .642178    -0.87   0.399    -1.947028    .8276548
      route1_yr1857 |  -.2718868   .2938533    -0.93   0.372    -.9067182    .3629446
      route1_yr1858 |   -.169699   .3162183    -0.54   0.601    -.8528472    .5134492
      route1_yr1859 |    .363833   .1473763     2.47   0.028     .0454459      .68222
      route1_yr1860 |   .3276846   .6252631     0.52   0.609    -1.023114    1.678483
      route1_yr1861 |   .3002366   .5130049     0.59   0.568    -.8080432    1.408516
      route1_yr1862 |   .3001705   .3355398     0.89   0.387    -.4247191     1.02506
      route1_yr1863 |   .3297969   .2834623     1.16   0.266    -.2825862    .9421801
      route1_yr1864 |   .5973335   .6395854     0.93   0.367    -.7844069    1.979074
dist_nanjing_yr1850 |  -.8623218   1.125332    -0.77   0.457    -3.293454     1.56881
dist_nanjing_yr1851 |  -.4639161   1.279308    -0.36   0.723    -3.227693    2.299861
dist_nanjing_yr1852 |  -.8130649   1.110507    -0.73   0.477    -3.212169     1.58604
dist_nanjing_yr1854 |  -.3468791   1.560172    -0.22   0.828    -3.717426    3.023667
dist_nanjing_yr1855 |   .6593453     2.4166     0.27   0.789    -4.561401    5.880092
dist_nanjing_yr1856 |  -.2937924    1.25163    -0.23   0.818    -2.997775     2.41019
dist_nanjing_yr1857 |  -1.987296   1.183575    -1.68   0.117    -4.544255    .5696637
dist_nanjing_yr1858 |  -1.946977   .7000992    -2.78   0.016    -3.459449   -.4345046
dist_nanjing_yr1859 |  -2.744594    .978286    -2.81   0.015    -4.858052   -.6311354
dist_nanjing_yr1860 |  -.9565231   1.343497    -0.71   0.489    -3.858972    1.945925
dist_nanjing_yr1861 |  -4.474575   .7405842    -6.04   0.000     -6.07451    -2.87464
dist_nanjing_yr1862 |  -3.763025   .9756445    -3.86   0.002    -5.870777   -1.655273
dist_nanjing_yr1863 |  -3.915141   .8042743    -4.87   0.000     -5.65267   -2.177612
dist_nanjing_yr1864 |  -3.257151   .9076481    -3.59   0.003    -5.218005   -1.296296
     mainriv_yr1850 |  -.3869652   .2897902    -1.34   0.205    -1.013019    .2390885
     mainriv_yr1851 |  -.3400825   .2743859    -1.24   0.237    -.9328573    .2526923
     mainriv_yr1852 |  -.2387684   .3238674    -0.74   0.474    -.9384414    .4609047
     mainriv_yr1854 |   -.936969   .8769175    -1.07   0.305    -2.831434    .9574962
     mainriv_yr1855 |  -1.431044   .5054309    -2.83   0.014    -2.522961   -.3391271
     mainriv_yr1856 |  -1.431874   .5132227    -2.79   0.015    -2.540624   -.3231237
     mainriv_yr1857 |  -1.134208    .496047    -2.29   0.040    -2.205852   -.0625637
     mainriv_yr1858 |  -.5449523   .4357281    -1.25   0.233    -1.486286     .396381
     mainriv_yr1859 |  -1.198928   .3633309    -3.30   0.006    -1.983856   -.4139992
     mainriv_yr1860 |  -1.119689   .5492125    -2.04   0.062     -2.30619    .0668126
     mainriv_yr1861 |  -.8655083   .3828858    -2.26   0.042    -1.692683   -.0383338
     mainriv_yr1862 |  -.3506807   .4813069    -0.73   0.479    -1.390481    .6891196
     mainriv_yr1863 |  -.8058671   .3601477    -2.24   0.043    -1.583919   -.0278153
     mainriv_yr1864 |  -.5721616   .4645522    -1.23   0.240    -1.575766    .4314424
  dist2canal_yr1850 |   .5142309   .9067291     0.57   0.580    -1.444638      2.4731
  dist2canal_yr1851 |   .2231873   1.038662     0.21   0.833    -2.020707    2.467081
  dist2canal_yr1852 |   .9371677   1.000433     0.94   0.366    -1.224136    3.098471
  dist2canal_yr1854 |   .3175508   1.559717     0.20   0.842    -3.052014    3.687115
  dist2canal_yr1855 |  -1.322667   2.146716    -0.62   0.548    -5.960364    3.315031
  dist2canal_yr1856 |  -1.333635   .9183476    -1.45   0.170    -3.317604    .6503341
  dist2canal_yr1857 |   .6108785   1.121078     0.54   0.595    -1.811062    3.032819
  dist2canal_yr1858 |   1.299046   .5508788     2.36   0.035     .1089449    2.489148
  dist2canal_yr1859 |   1.563062   .9210347     1.70   0.113    -.4267125    3.552836
  dist2canal_yr1860 |    .469303   1.241406     0.38   0.712    -2.212592    3.151198
  dist2canal_yr1861 |   3.350233   .6659675     5.03   0.000     1.911498    4.788968
  dist2canal_yr1862 |   2.803425   .8997339     3.12   0.008     .8596676    4.747182
  dist2canal_yr1863 |   2.576393   .6632353     3.88   0.002      1.14356    4.009226
  dist2canal_yr1864 |   2.317146   .7529533     3.08   0.009     .6904894    3.943803
     lnwheat_yr1850 |   3.741318   3.631547     1.03   0.322    -4.104161     11.5868
     lnwheat_yr1851 |   2.026349   3.044277     0.67   0.517     -4.55041    8.603109
     lnwheat_yr1852 |  -1.852136   4.015298    -0.46   0.652    -10.52666    6.822388
     lnwheat_yr1854 |   8.943219   4.021702     2.22   0.045     .2548596    17.63158
     lnwheat_yr1855 |    7.31903   6.223919     1.18   0.261     -6.12693    20.76499
     lnwheat_yr1856 |   10.96826   1.725986     6.35   0.000     7.239495    14.69703
     lnwheat_yr1857 |   7.001385   4.527466     1.55   0.146    -2.779611    16.78238
     lnwheat_yr1858 |   7.193737   2.865357     2.51   0.026     1.003509    13.38396
     lnwheat_yr1859 |  -.7671841    3.55701    -0.22   0.833    -8.451637    6.917268
     lnwheat_yr1860 |   1.861819   2.201764     0.85   0.413    -2.894803    6.618441
     lnwheat_yr1861 |   2.217065   3.695301     0.60   0.559    -5.766147    10.20028
     lnwheat_yr1862 |   3.769188   4.105812     0.92   0.375    -5.100881    12.63926
     lnwheat_yr1863 |   9.948108   3.712051     2.68   0.019      1.92871    17.96751
     lnwheat_yr1864 |   6.741318   2.188141     3.08   0.009     2.014127    11.46851
      lnrice_yr1850 |   -3.52426    1.87077    -1.88   0.082    -7.565813     .517293
      lnrice_yr1851 |  -2.957083   2.261382    -1.31   0.214    -7.842501    1.928336
      lnrice_yr1852 |  -.3840808   2.768449    -0.14   0.892    -6.364952     5.59679
      lnrice_yr1854 |  -2.259147   3.552908    -0.64   0.536    -9.934738    5.416443
      lnrice_yr1855 |  -4.697278   5.191174    -0.90   0.382    -15.91213    6.517572
      lnrice_yr1856 |  -5.335624   3.145425    -1.70   0.114     -12.1309    1.459653
      lnrice_yr1857 |  -6.500065   2.117862    -3.07   0.009    -11.07543   -1.924702
      lnrice_yr1858 |  -4.700774   1.972116    -2.38   0.033    -8.961272   -.4402769
      lnrice_yr1859 |  -6.860125   2.788195    -2.46   0.029    -12.88365   -.8365969
      lnrice_yr1860 |  -5.516268   2.751009    -2.01   0.066    -11.45946    .4269251
      lnrice_yr1861 |  -3.192539   2.672951    -1.19   0.254      -8.9671    2.582021
      lnrice_yr1862 |  -6.688309    2.82301    -2.37   0.034    -12.78705   -.5895659
      lnrice_yr1863 |  -8.623915     2.3085    -3.74   0.002    -13.61113   -3.636704
      lnrice_yr1864 |  -7.417611    2.23568    -3.32   0.006     -12.2475   -2.587718
       lnpop_yr1850 |   .0710202   .4670317     0.15   0.881    -.9379404    1.079981
       lnpop_yr1851 |   .2167103   .5199856     0.42   0.684    -.9066503    1.340071
       lnpop_yr1852 |  -.1945056   .6240121    -0.31   0.760    -1.542602    1.153591
       lnpop_yr1854 |   .3163947    .637502     0.50   0.628    -1.060844    1.693634
       lnpop_yr1855 |   .5396741   .9601435     0.56   0.584     -1.53459    2.613938
       lnpop_yr1856 |  -.3226775   .7683941    -0.42   0.681    -1.982692    1.337337
       lnpop_yr1857 |  -.2794395   .6063534    -0.46   0.653    -1.589386    1.030507
       lnpop_yr1858 |   .0537512   .6085049     0.09   0.931    -1.260844    1.368346
       lnpop_yr1859 |   .5980031   .6365258     0.94   0.365    -.7771272    1.973133
       lnpop_yr1860 |   .2405869   .7999133     0.30   0.768    -1.487521    1.968695
       lnpop_yr1861 |  -.9833516   .6785722    -1.45   0.171    -2.449318    .4826144
       lnpop_yr1862 |   -.748511   .7636178    -0.98   0.345    -2.398207    .9011851
       lnpop_yr1863 |   .3505223   .4663268     0.75   0.466    -.6569155     1.35796
       lnpop_yr1864 |  -.0784898   .4494484    -0.17   0.864    -1.049464    .8924844
      lnarea_yr1850 |  -.1193598   .3414901    -0.35   0.732    -.8571044    .6183847
      lnarea_yr1851 |  -.2478043   .2514991    -0.99   0.342     -.791135    .2955264
      lnarea_yr1852 |  -.3437888   .4166055    -0.83   0.424     -1.24381    .5562327
      lnarea_yr1854 |   .1528411   .7110945     0.21   0.833    -1.383385    1.689067
      lnarea_yr1855 |    .271809   .5355217     0.51   0.620    -.8851153    1.428733
      lnarea_yr1856 |    .521788   .6269499     0.83   0.420    -.8326548    1.876231
      lnarea_yr1857 |   .8232632   .7082155     1.16   0.266    -.7067435     2.35327
      lnarea_yr1858 |   .5486434    .584259     0.94   0.365    -.7135714    1.810858
      lnarea_yr1859 |   .2007504   .5545999     0.36   0.723    -.9973899    1.398891
      lnarea_yr1860 |   .5003745   .5761627     0.87   0.401    -.7443494    1.745098
      lnarea_yr1861 |    1.46087   .8196493     1.78   0.098    -.3098748    3.231615
      lnarea_yr1862 |   1.203631   .7190159     1.67   0.118    -.3497086     2.75697
      lnarea_yr1863 |   1.255554   .5799705     2.16   0.050     .0026043    2.508504
      lnarea_yr1864 |   1.063281   .5251949     2.02   0.064    -.0713341    2.197895
     Zeng_all0_Post |   .1818727   .0502896     3.62   0.003     .0732285    .2905169
              _cons |   2.498621   9.371963     0.27   0.794    -17.74828    22.74552
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_Post)  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

. 
. 
. 
. *** *** *** 
. xi: reghdfe  lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_
> Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post      Zeng_all0_pc_Post , a
> bsorb(year cntyid prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     13) =    3634.43
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6691
                                                  Adj R-squared   =     0.5402
Number of clusters (prefid)  =         14         Within R-sq.    =     0.1137
Number of clusters (cntyid)  =         74         Root MSE        =     1.1198

                              (Std. Err. adjusted for 14 clusters in prefid cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     capital_Post |  -1.361217   .3035553    -4.48   0.001    -2.017008   -.7054253
  lnurbanpop_Post |    .438591   .1090621     4.02   0.001     .2029768    .6742053
    lnjinshi_Post |   .3472428   .1366845     2.54   0.025     .0519538    .6425318
    lnquotas_Post |  -1.165929   .4687858    -2.49   0.027    -2.178679   -.1531785
      route1_Post |  -.0483474   .1203826    -0.40   0.694    -.3084181    .2117233
dist_nanjing_Post |  -1.211584   .7542177    -1.61   0.132    -2.840972    .4178048
     mainriv_Post |  -.6528719   .2153104    -3.03   0.010    -1.118022   -.1877221
  dist2canal_Post |   .4237847   .8648669     0.49   0.632    -1.444647    2.292216
     lnwheat_Post |    5.39865   1.577618     3.42   0.005     1.990414    8.806886
      lnrice_Post |  -3.060719   .7721073    -3.96   0.002    -4.728756   -1.392683
       lnpop_Post |   .1889792   .3508268     0.54   0.599     -.568936    .9468943
      lnarea_Post |   .8476787   .3655696     2.32   0.037     .0579136    1.637444
Zeng_all0_pc_Post |   .0625137   .0165612     3.77   0.002     .0267353    .0982921
            _cons |  -3.780746   4.014946    -0.94   0.364    -12.45451    4.893016
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

. 
. 
. 
. xi: reghdfe  lnmartyr1  capital_yr* lnurbanpop_yr*  lnjinshi_yr*  lnquotas_yr* route1_yr*  dist_nanjing_yr* m
> ainriv_yr* dist2canal_yr*   lnwheat_yr* lnrice_yr* lnpop_yr* lnarea_yr*    Zeng_all0_pc_Post , absorb(year cn
> tyid  prefidXyear)   cluster(prefid cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F( 169,     13) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7437
                                                  Adj R-squared   =     0.5573
Number of clusters (prefid)  =         14         Within R-sq.    =     0.3135
Number of clusters (cntyid)  =         74         Root MSE        =     1.0987

                                (Std. Err. adjusted for 14 clusters in prefid cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
          lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
     capital_yr1850 |  -.3829612   .4996723    -0.77   0.457    -1.462438    .6965152
     capital_yr1851 |    -.23679   .4688275    -0.51   0.622     -1.24963    .7760503
     capital_yr1852 |   .2435674   .9598545     0.25   0.804    -1.830072    2.317207
     capital_yr1854 |  -.1772054   1.078611    -0.16   0.872    -2.507402    2.152991
     capital_yr1855 |  -.9121985   .6807708    -1.34   0.203    -2.382914    .5585175
     capital_yr1856 |  -1.116862   .6112073    -1.83   0.091    -2.437296    .2035708
     capital_yr1857 |   -1.39349   .6164707    -2.26   0.042    -2.725294   -.0616859
     capital_yr1858 |  -1.382652   .5298031    -2.61   0.022    -2.527222   -.2380822
     capital_yr1859 |   -1.50709   .5404775    -2.79   0.015     -2.67472   -.3394588
     capital_yr1860 |  -1.645403   .4554593    -3.61   0.003    -2.629363   -.6614432
     capital_yr1861 |  -2.559008     .93351    -2.74   0.017    -4.575734   -.5422826
     capital_yr1862 |  -1.439608    .623143    -2.31   0.038    -2.785827   -.0933897
     capital_yr1863 |  -2.098105   .5649765    -3.71   0.003    -3.318662   -.8775472
     capital_yr1864 |  -1.776267   .4694448    -3.78   0.002     -2.79044    -.762093
  lnurbanpop_yr1850 |   .2577839   .2527556     1.02   0.326    -.2882614    .8038292
  lnurbanpop_yr1851 |   .1590526   .2211316     0.72   0.485    -.3186732    .6367784
  lnurbanpop_yr1852 |   .1738866   .3980696     0.44   0.669    -.6860905    1.033864
  lnurbanpop_yr1854 |   .1990424   .3129446     0.64   0.536    -.4770332     .875118
  lnurbanpop_yr1855 |   .4904987   .2559296     1.92   0.078    -.0624036    1.043401
  lnurbanpop_yr1856 |   .2894724   .3365833     0.86   0.405    -.4376717    1.016616
  lnurbanpop_yr1857 |   .5156134   .1851645     2.78   0.015     .1155898     .915637
  lnurbanpop_yr1858 |    .593356   .1923447     3.08   0.009     .1778205    1.008891
  lnurbanpop_yr1859 |   .5202823   .1915753     2.72   0.018      .106409    .9341557
  lnurbanpop_yr1860 |   .6528902   .2172777     3.00   0.010     .1834903     1.12229
  lnurbanpop_yr1861 |   .8875207   .3516601     2.52   0.025     .1278053    1.647236
  lnurbanpop_yr1862 |   .6242711   .2307855     2.70   0.018     .1256893    1.122853
  lnurbanpop_yr1863 |   .8297624   .3033267     2.74   0.017     .1744649     1.48506
  lnurbanpop_yr1864 |   .8462806   .2003676     4.22   0.001     .4134126    1.279148
    lnjinshi_yr1850 |   .2537009   .3406942     0.74   0.470    -.4823241    .9897258
    lnjinshi_yr1851 |   .1740527   .3249082     0.54   0.601    -.5278688    .8759742
    lnjinshi_yr1852 |  -.3780894   .2750829    -1.37   0.193    -.9723699    .2161911
    lnjinshi_yr1854 |   .1106176   .1609405     0.69   0.504    -.2370731    .4583083
    lnjinshi_yr1855 |   .1194165    .211413     0.56   0.582    -.3373134    .5761465
    lnjinshi_yr1856 |   .4067846    .266328     1.53   0.151    -.1685822    .9821513
    lnjinshi_yr1857 |   .5631196   .3380161     1.67   0.120    -.1671199    1.293359
    lnjinshi_yr1858 |   .2372361   .2305482     1.03   0.322     -.260833    .7353053
    lnjinshi_yr1859 |   .2684012   .2368867     1.13   0.278    -.2433615    .7801639
    lnjinshi_yr1860 |    .323494   .2402933     1.35   0.201    -.1956282    .8426162
    lnjinshi_yr1861 |   .4753679   .3616301     1.31   0.211    -.3058865    1.256622
    lnjinshi_yr1862 |   .4757417   .4667392     1.02   0.327     -.532587    1.484071
    lnjinshi_yr1863 |   .4699834   .3947649     1.19   0.255    -.3828542    1.322821
    lnjinshi_yr1864 |   .5060845   .4114571     1.23   0.240    -.3828144    1.394983
    lnquotas_yr1850 |   -.579331   .6705271    -0.86   0.403    -2.027917    .8692548
    lnquotas_yr1851 |  -.7032065   .5849721    -1.20   0.251    -1.966962    .5605489
    lnquotas_yr1852 |   .0876009   .7615393     0.12   0.910    -1.557605    1.732807
    lnquotas_yr1854 |   .0331808   1.044668     0.03   0.975    -2.223688    2.290049
    lnquotas_yr1855 |  -1.849515   .6355901    -2.91   0.012    -3.222624   -.4764065
    lnquotas_yr1856 |  -.8884759    1.41533    -0.63   0.541     -3.94611    2.169158
    lnquotas_yr1857 |  -1.068759   .6397263    -1.67   0.119    -2.450804    .3132853
    lnquotas_yr1858 |  -1.870434   .6188329    -3.02   0.010    -3.207342   -.5335271
    lnquotas_yr1859 |   -2.16445   .6950235    -3.11   0.008    -3.665957   -.6629433
    lnquotas_yr1860 |  -1.979603   1.142095    -1.73   0.107    -4.446949     .487743
    lnquotas_yr1861 |  -1.111637   .8222148    -1.35   0.199    -2.887924    .6646502
    lnquotas_yr1862 |  -1.486212   .9367132    -1.59   0.137    -3.509858    .5374338
    lnquotas_yr1863 |  -1.529036   .5582657    -2.74   0.017    -2.735096   -.3229765
    lnquotas_yr1864 |  -2.196348   .9605476    -2.29   0.040    -4.271485   -.1212106
      route1_yr1850 |   .2883738   .4176516     0.69   0.502    -.6139076    1.190655
      route1_yr1851 |    .104426   .4119877     0.25   0.804    -.7856194    .9944714
      route1_yr1852 |   .7799262   .5456218     1.43   0.176    -.3988181    1.958671
      route1_yr1854 |  -.1974885   .5160631    -0.38   0.708    -1.312375    .9173982
      route1_yr1855 |   .6346745   .4746758     1.34   0.204    -.3908001    1.660149
      route1_yr1856 |  -.4443302   .7456991    -0.60   0.562    -2.055315    1.166655
      route1_yr1857 |  -.1565306   .3609389    -0.43   0.672    -.9362915    .6232304
      route1_yr1858 |  -.0543428   .4297699    -0.13   0.901    -.9828042    .8741187
      route1_yr1859 |   .4791892   .1767248     2.71   0.018     .0973984      .86098
      route1_yr1860 |   .4430408   .7092788     0.62   0.543    -1.089263    1.975345
      route1_yr1861 |   .4155928    .621903     0.67   0.516    -.9279469    1.759132
      route1_yr1862 |   .4155268   .4364083     0.95   0.358    -.5272761     1.35833
      route1_yr1863 |   .4451532   .3665269     1.21   0.246      -.34668    1.236986
      route1_yr1864 |   .7126897   .7427865     0.96   0.355    -.8920029    2.317382
dist_nanjing_yr1850 |  -.8623218   1.125332    -0.77   0.457    -3.293454     1.56881
dist_nanjing_yr1851 |  -.4639161   1.279308    -0.36   0.723    -3.227693    2.299861
dist_nanjing_yr1852 |  -.8130649   1.110507    -0.73   0.477    -3.212169     1.58604
dist_nanjing_yr1854 |   .0000396   1.442053     0.00   1.000    -3.115327    3.115406
dist_nanjing_yr1855 |   1.006264   2.375901     0.42   0.679    -4.126559    6.139087
dist_nanjing_yr1856 |   .0531263   1.249527     0.04   0.967    -2.646312    2.752564
dist_nanjing_yr1857 |  -1.640377   1.177408    -1.39   0.187    -4.184013    .9032593
dist_nanjing_yr1858 |  -1.600058   .6247413    -2.56   0.024     -2.94973   -.2503867
dist_nanjing_yr1859 |  -2.397675   1.069338    -2.24   0.043    -4.707839   -.0875116
dist_nanjing_yr1860 |  -.6096044   1.296654    -0.47   0.646    -3.410855    2.191646
dist_nanjing_yr1861 |  -4.127657   .6786096    -6.08   0.000    -5.593703    -2.66161
dist_nanjing_yr1862 |  -3.416106   .7987514    -4.28   0.001    -5.141704   -1.690509
dist_nanjing_yr1863 |  -3.568222   .7024185    -5.08   0.000    -5.085705   -2.050739
dist_nanjing_yr1864 |  -2.910232   .7530701    -3.86   0.002    -4.537141   -1.283323
     mainriv_yr1850 |  -.3869652   .2897902    -1.34   0.205    -1.013019    .2390885
     mainriv_yr1851 |  -.3400825   .2743859    -1.24   0.237    -.9328573    .2526923
     mainriv_yr1852 |  -.2387684   .3238674    -0.74   0.474    -.9384414    .4609047
     mainriv_yr1854 |  -.8865783   .8302592    -1.07   0.305    -2.680244    .9070877
     mainriv_yr1855 |  -1.380653   .4989171    -2.77   0.016    -2.458498   -.3028087
     mainriv_yr1856 |  -1.381483   .4995636    -2.77   0.016    -2.460725   -.3022418
     mainriv_yr1857 |  -1.083817   .4743455    -2.28   0.040    -2.108579   -.0590563
     mainriv_yr1858 |  -.4945617   .4091108    -1.21   0.248    -1.378392    .3892686
     mainriv_yr1859 |  -1.148537   .3775357    -3.04   0.009    -1.964153   -.3329209
     mainriv_yr1860 |  -1.069298   .5132299    -2.08   0.058    -2.178064    .0394676
     mainriv_yr1861 |  -.8151177   .3857286    -2.11   0.054    -1.648434    .0181983
     mainriv_yr1862 |  -.3002901    .473422    -0.63   0.537    -1.323056    .7224761
     mainriv_yr1863 |  -.7554764   .3201942    -2.36   0.035    -1.447214   -.0637389
     mainriv_yr1864 |   -.521771   .4227246    -1.23   0.239    -1.435012      .39147
  dist2canal_yr1850 |   .5142309   .9067291     0.57   0.580    -1.444638      2.4731
  dist2canal_yr1851 |   .2231873   1.038662     0.21   0.833    -2.020707    2.467081
  dist2canal_yr1852 |   .9371677   1.000433     0.94   0.366    -1.224136    3.098471
  dist2canal_yr1854 |   .0099151   1.459904     0.01   0.995    -3.144016    3.163846
  dist2canal_yr1855 |  -1.630302   2.096777    -0.78   0.451    -6.160113    2.899509
  dist2canal_yr1856 |  -1.641271   .9230483    -1.78   0.099    -3.635396    .3528539
  dist2canal_yr1857 |   .3032428   1.124199     0.27   0.792    -2.125442    2.731928
  dist2canal_yr1858 |   .9914106   .5184399     1.91   0.078    -.1286107    2.111432
  dist2canal_yr1859 |   1.255426   .9829684     1.28   0.224     -.868148       3.379
  dist2canal_yr1860 |   .1616673   1.209025     0.13   0.896    -2.450273    2.773608
  dist2canal_yr1861 |   3.042597   .5885631     5.17   0.000     1.771084     4.31411
  dist2canal_yr1862 |   2.495789   .7615521     3.28   0.006     .8505557    4.141022
  dist2canal_yr1863 |   2.268757   .5933231     3.82   0.002     .9869604    3.550554
  dist2canal_yr1864 |   2.009511   .6084383     3.30   0.006     .6950596    3.323962
     lnwheat_yr1850 |   3.741318   3.631547     1.03   0.322    -4.104161     11.5868
     lnwheat_yr1851 |   2.026349   3.044277     0.67   0.517     -4.55041    8.603109
     lnwheat_yr1852 |  -1.852136   4.015298    -0.46   0.652    -10.52666    6.822388
     lnwheat_yr1854 |   9.393848   4.147133     2.27   0.041      .434512    18.35318
     lnwheat_yr1855 |   7.769659   6.375736     1.22   0.245    -6.004282     21.5436
     lnwheat_yr1856 |   11.41889   1.923271     5.94   0.000     7.263916    15.57386
     lnwheat_yr1857 |   7.452014   4.070776     1.83   0.090    -1.342363    16.24639
     lnwheat_yr1858 |   7.644366   2.643577     2.89   0.013     1.933265    13.35547
     lnwheat_yr1859 |  -.3165551   3.416215    -0.09   0.928     -7.69684     7.06373
     lnwheat_yr1860 |   2.312448   2.035914     1.14   0.277    -2.085876    6.710772
     lnwheat_yr1861 |   2.667694   3.676586     0.73   0.481    -5.275087    10.61048
     lnwheat_yr1862 |   4.219817   4.030965     1.05   0.314    -4.488554    12.92819
     lnwheat_yr1863 |   10.39874    3.65611     2.84   0.014     2.500191    18.29728
     lnwheat_yr1864 |   7.191947     2.2151     3.25   0.006     2.406514    11.97738
      lnrice_yr1850 |   -3.52426    1.87077    -1.88   0.082    -7.565813     .517293
      lnrice_yr1851 |  -2.957083   2.261382    -1.31   0.214    -7.842501    1.928336
      lnrice_yr1852 |  -.3840808   2.768449    -0.14   0.892    -6.364952     5.59679
      lnrice_yr1854 |  -1.418799   3.231547    -0.44   0.668    -8.400131    5.562532
      lnrice_yr1855 |   -3.85693   5.095707    -0.76   0.463    -14.86553    7.151676
      lnrice_yr1856 |  -4.495276    3.16561    -1.42   0.179    -11.33416    2.343609
      lnrice_yr1857 |  -5.659717   2.120826    -2.67   0.019    -10.24148   -1.077951
      lnrice_yr1858 |  -3.860426   1.803068    -2.14   0.052    -7.755718    .0348651
      lnrice_yr1859 |  -6.019777   3.107506    -1.94   0.075    -12.73314    .6935821
      lnrice_yr1860 |   -4.67592   2.407901    -1.94   0.074    -9.877873    .5260332
      lnrice_yr1861 |  -2.352191   2.495733    -0.94   0.363    -7.743895    3.039513
      lnrice_yr1862 |  -5.847961   2.229027    -2.62   0.021    -10.66348    -1.03244
      lnrice_yr1863 |  -7.783567   1.942663    -4.01   0.001    -11.98043     -3.5867
      lnrice_yr1864 |  -6.577264   1.787463    -3.68   0.003    -10.43884   -2.715685
       lnpop_yr1850 |   .0710202   .4670317     0.15   0.881    -.9379404    1.079981
       lnpop_yr1851 |   .2167103   .5199856     0.42   0.684    -.9066503    1.340071
       lnpop_yr1852 |  -.1945056   .6240121    -0.31   0.760    -1.542602    1.153591
       lnpop_yr1854 |   .5571835    .575129     0.97   0.350    -.6853072    1.799674
       lnpop_yr1855 |   .7804629   .9281453     0.84   0.416    -1.224673    2.785599
       lnpop_yr1856 |  -.0818888   .7714315    -0.11   0.917    -1.748465    1.584688
       lnpop_yr1857 |  -.0386507   .5903897    -0.07   0.949     -1.31411    1.236809
       lnpop_yr1858 |   .2945399     .59475     0.50   0.629    -.9903393    1.579419
       lnpop_yr1859 |   .8387919   .6813246     1.23   0.240    -.6331205    2.310704
       lnpop_yr1860 |   .4813757   .7096813     0.68   0.509    -1.051798    2.014549
       lnpop_yr1861 |  -.7425629   .6350581    -1.17   0.263    -2.114522    .6293967
       lnpop_yr1862 |  -.5077222   .7415056    -0.68   0.506    -2.109648    1.094203
       lnpop_yr1863 |    .591311   .3793036     1.56   0.143    -.2281245    1.410747
       lnpop_yr1864 |   .1622989   .4001335     0.41   0.692    -.7021369    1.026735
      lnarea_yr1850 |  -.1193598   .3414901    -0.35   0.732    -.8571044    .6183847
      lnarea_yr1851 |  -.2478043   .2514991    -0.99   0.342     -.791135    .2955264
      lnarea_yr1852 |  -.3437888   .4166055    -0.83   0.424     -1.24381    .5562327
      lnarea_yr1854 |   .0952537   .7278804     0.13   0.898    -1.477236    1.667744
      lnarea_yr1855 |   .2142216   .4892633     0.44   0.669    -.8427675    1.271211
      lnarea_yr1856 |   .4642007   .6232311     0.74   0.470    -.8822082     1.81061
      lnarea_yr1857 |   .7656758   .6912292     1.11   0.288     -.727634    2.258986
      lnarea_yr1858 |   .4910561   .5584994     0.88   0.395    -.7155085    1.697621
      lnarea_yr1859 |    .143163   .5452787     0.26   0.797     -1.03484    1.321166
      lnarea_yr1860 |   .4427872   .5473673     0.81   0.433    -.7397279    1.625302
      lnarea_yr1861 |   1.403283   .8267423     1.70   0.113    -.3827854    3.189351
      lnarea_yr1862 |   1.146043   .6962899     1.65   0.124    -.3581995    2.650286
      lnarea_yr1863 |   1.197967   .5591091     2.14   0.052    -.0099147    2.405849
      lnarea_yr1864 |   1.005693   .4880859     2.06   0.060    -.0487523    2.060139
  Zeng_all0_pc_Post |   .0625137    .017902     3.49   0.004     .0238388    .1011886
              _cons |  -.8827866   8.657756    -0.10   0.920    -19.58673    17.82116
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210         210           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\\Appendix_Table_B1_IV.doc, keep(Zeng_all0_pc_Post )  se  bdec(3) rdec(3) nocons append
Results\\Appendix_Table_B1_IV.doc
dir : seeout

.   
. 
. 
end of do-file

. 
. 
. ******* Table B.2. Yearly Effects of Elite Connections
. 
. 
. do Programs\Appendix_Table_B2.do

. 
. *********************************************************************************
. *** Table B.2. Yearly Effects of Elite Connections
. *********************************************************************************
. 
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************** ********************** ********************** **********************
. ********************** gen year dummies
. 
. 
. tab year, gen(year)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1850 |         75        6.67        6.67
       1851 |         75        6.67       13.33
       1852 |         75        6.67       20.00
       1853 |         75        6.67       26.67
       1854 |         75        6.67       33.33
       1855 |         75        6.67       40.00
       1856 |         75        6.67       46.67
       1857 |         75        6.67       53.33
       1858 |         75        6.67       60.00
       1859 |         75        6.67       66.67
       1860 |         75        6.67       73.33
       1861 |         75        6.67       80.00
       1862 |         75        6.67       86.67
       1863 |         75        6.67       93.33
       1864 |         75        6.67      100.00
------------+-----------------------------------
      Total |      1,125      100.00

. local r=1 

. while `r'<16 {
  2. local s=`r'+1849
  3. rename year`r' yr`s'
  4. local r=`r'+1
  5. }

. 
. **
. foreach y of varlist    Zeng_all0_invdist Zeng_all0 Zeng_all0_invdist_pc Zeng_all0_pc{
  2. foreach x of varlist yr1850-yr1852 yr1854-yr1864 {
  3. gen `y'_`x'=`y'*`x'
  4. }
  5. }

. 
. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** Weighted
. 
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_invdist_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post ro
> ute1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   
>    , absorb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       6.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6711
                                                  Adj R-squared   =     0.5440
                                                  Within R-sq.    =     0.1190
Number of clusters (cntyid)  =         74         Root MSE        =     1.1151

                                            (Std. Err. adjusted for 74 clusters in cntyid)
------------------------------------------------------------------------------------------
                         |               Robust
               lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
Zeng_all0_invdist_yr1850 |  -.0024709   .0522458    -0.05   0.962    -.1065965    .1016547
Zeng_all0_invdist_yr1851 |  -.0164508   .0494299    -0.33   0.740    -.1149646    .0820629
Zeng_all0_invdist_yr1852 |   .0297401   .0836645     0.36   0.723     -.137003    .1964831
Zeng_all0_invdist_yr1854 |   .2872017   .0876611     3.28   0.002     .1124935    .4619099
Zeng_all0_invdist_yr1855 |   .2323828   .1084022     2.14   0.035     .0163375    .4484281
Zeng_all0_invdist_yr1856 |   .1561531   .1178838     1.32   0.189    -.0787889    .3910951
Zeng_all0_invdist_yr1857 |   .2834177   .0867142     3.27   0.002     .1105966    .4562387
Zeng_all0_invdist_yr1858 |   .3125987   .0855649     3.65   0.000      .142068    .4831293
Zeng_all0_invdist_yr1859 |    .254592   .0835678     3.05   0.003     .0880416    .4211423
Zeng_all0_invdist_yr1860 |   .2401093   .0874939     2.74   0.008     .0657342    .4144844
Zeng_all0_invdist_yr1861 |   .2421287   .0804964     3.01   0.004     .0816995    .4025578
Zeng_all0_invdist_yr1862 |   .2877558   .0833906     3.45   0.001     .1215585     .453953
Zeng_all0_invdist_yr1863 |   .2960503   .0856566     3.46   0.001     .1253369    .4667637
Zeng_all0_invdist_yr1864 |   .3131157   .0861566     3.63   0.001     .1414058    .4848256
            capital_Post |  -1.432147   .4275203    -3.35   0.001    -2.284193   -.5800999
         lnurbanpop_Post |   .4336873   .1371317     3.16   0.002     .1603843    .7069903
           lnjinshi_Post |   .3889764   .1435955     2.71   0.008      .102791    .6751619
           lnquotas_Post |  -.8923757   .5377417    -1.66   0.101    -1.964093    .1793419
             route1_Post |  -.1870466   .2492757    -0.75   0.455    -.6838523    .3097591
       dist_nanjing_Post |  -1.389248   .9876837    -1.41   0.164    -3.357699    .5792024
            mainriv_Post |  -.7111534   .2727249    -2.61   0.011    -1.254693   -.1676134
         dist2canal_Post |    .551294   1.061714     0.52   0.605    -1.564698    2.667287
            lnwheat_Post |   5.029848   2.456125     2.05   0.044     .1347981    9.924898
             lnrice_Post |   -4.04189   1.455684    -2.78   0.007    -6.943064   -1.140715
              lnpop_Post |  -.0872555   .3436365    -0.25   0.800     -.772122     .597611
             lnarea_Post |   .8448603     .33093     2.55   0.013     .1853177    1.504403
                   _cons |   .1722239   4.826257     0.04   0.972    -9.446492     9.79094
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B2.doc, keep(Zeng_all0_invdist_yr* )   se  bdec(3) rdec(3) nocons addtex
> t(Observations, `e(N_full)') noobs  replace 
Results\Appendix_Table_B2.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_invdist, replace)
file Results\Placebo_Yearly_ctrl_all0_invdist.dta saved

. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** unweighted
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Pos
> t  dist_nanjing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     , abso
> rb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       6.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6704
                                                  Adj R-squared   =     0.5430
                                                  Within R-sq.    =     0.1170
Number of clusters (cntyid)  =         74         Root MSE        =     1.1163

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
 Zeng_all0_yr1850 |   .0098665    .033781     0.29   0.771    -.0574589    .0771919
 Zeng_all0_yr1851 |   -.001187   .0312959    -0.04   0.970    -.0635597    .0611857
 Zeng_all0_yr1852 |   .0256354   .0565717     0.45   0.652    -.0871118    .1383826
 Zeng_all0_yr1854 |   .2119564   .0616577     3.44   0.001     .0890729      .33484
 Zeng_all0_yr1855 |   .1634491   .0780445     2.09   0.040     .0079067    .3189914
 Zeng_all0_yr1856 |   .1149849   .0847825     1.36   0.179    -.0539865    .2839562
 Zeng_all0_yr1857 |   .2066915   .0614515     3.36   0.001     .0842189    .3291641
 Zeng_all0_yr1858 |   .2153759   .0608531     3.54   0.001     .0940959    .3366559
 Zeng_all0_yr1859 |   .1929583   .0575994     3.35   0.001     .0781629    .3077537
 Zeng_all0_yr1860 |   .1875302   .0604424     3.10   0.003     .0670687    .3079918
 Zeng_all0_yr1861 |     .17755   .0580123     3.06   0.003     .0619317    .2931683
 Zeng_all0_yr1862 |    .201194   .0577336     3.48   0.001     .0861311    .3162568
 Zeng_all0_yr1863 |   .2069772    .063548     3.26   0.002     .0803262    .3336282
 Zeng_all0_yr1864 |   .2162984      .0703     3.08   0.003     .0761907     .356406
     capital_Post |  -1.388029    .420896    -3.30   0.002    -2.226873   -.5491844
  lnurbanpop_Post |   .4065032    .133003     3.06   0.003     .1414287    .6715778
    lnjinshi_Post |   .3379932   .1508445     2.24   0.028     .0373605     .638626
    lnquotas_Post |  -.9130775   .5490673    -1.66   0.101    -2.007367    .1812121
      route1_Post |  -.1637036   .2531433    -0.65   0.520    -.6682176    .3408103
dist_nanjing_Post |  -1.558502   .9933188    -1.57   0.121    -3.538184    .4211794
     mainriv_Post |  -.7032625   .2730541    -2.58   0.012    -1.247458   -.1590665
  dist2canal_Post |   .7314203   1.051455     0.70   0.489    -1.364125    2.826966
     lnwheat_Post |   4.948021   2.436897     2.03   0.046     .0912926     9.80475
      lnrice_Post |  -3.901067   1.450319    -2.69   0.009    -6.791548   -1.010586
       lnpop_Post |  -.0518096   .3440628    -0.15   0.881    -.7375258    .6339066
      lnarea_Post |    .905266   .3307484     2.74   0.008     .2460853    1.564447
            _cons |  -.4145257   4.955172    -0.08   0.934    -10.29017    9.461118
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B2.doc, keep(Zeng_all0_yr* )   se  bdec(3) rdec(3) nocons  addtext(Obser
> vations, `e(N_full)') noobs append 
Results\Appendix_Table_B2.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0, replace)
file Results\Placebo_Yearly_ctrl_all0.dta saved

. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** per capita weighted
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_invdist_pc_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post
>  route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post 
>      , absorb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       4.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6735
                                                  Adj R-squared   =     0.5474
                                                  Within R-sq.    =     0.1255
Number of clusters (cntyid)  =         74         Root MSE        =     1.1109

                                               (Std. Err. adjusted for 74 clusters in cntyid)
---------------------------------------------------------------------------------------------
                            |               Robust
                  lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_yr1850 |   .0054489    .017352     0.31   0.754    -.0291336    .0400315
Zeng_all0_invdist_pc_yr1851 |  -.0058021   .0156101    -0.37   0.711    -.0369129    .0253088
Zeng_all0_invdist_pc_yr1852 |   .0032408   .0347261     0.09   0.926    -.0659682    .0724498
Zeng_all0_invdist_pc_yr1854 |   .0949567   .0321884     2.95   0.004     .0308054     .159108
Zeng_all0_invdist_pc_yr1855 |   .0697685   .0344246     2.03   0.046     .0011603    .1383766
Zeng_all0_invdist_pc_yr1856 |   .0387974   .0373764     1.04   0.303    -.0356937    .1132885
Zeng_all0_invdist_pc_yr1857 |   .0938249   .0318083     2.95   0.004     .0304311    .1572187
Zeng_all0_invdist_pc_yr1858 |   .0937537   .0312806     3.00   0.004     .0314116    .1560958
Zeng_all0_invdist_pc_yr1859 |    .072344    .030833     2.35   0.022      .010894     .133794
Zeng_all0_invdist_pc_yr1860 |    .093601   .0299402     3.13   0.003     .0339304    .1532717
Zeng_all0_invdist_pc_yr1861 |   .0887268    .030921     2.87   0.005     .0271012    .1503523
Zeng_all0_invdist_pc_yr1862 |   .1175102   .0310462     3.79   0.000     .0556352    .1793851
Zeng_all0_invdist_pc_yr1863 |   .1073672   .0299785     3.58   0.001     .0476201    .1671143
Zeng_all0_invdist_pc_yr1864 |   .1038242   .0349045     2.97   0.004     .0342595    .1733888
               capital_Post |  -1.409048   .3833758    -3.68   0.000    -2.173115   -.6449813
            lnurbanpop_Post |   .4734599   .1405673     3.37   0.001     .1933096    .7536101
              lnjinshi_Post |   .4051828   .1363287     2.97   0.004     .1334801    .6768855
              lnquotas_Post |  -1.175197   .5334898    -2.20   0.031     -2.23844    -.111953
                route1_Post |  -.0724558   .2692708    -0.27   0.789    -.6091118    .4642002
          dist_nanjing_Post |  -.9831852   .9922057    -0.99   0.325    -2.960648    .9942778
               mainriv_Post |  -.6709632   .2754552    -2.44   0.017    -1.219945   -.1219818
            dist2canal_Post |   .1890491   1.079485     0.18   0.861    -1.962362     2.34046
               lnwheat_Post |   5.687431   2.337597     2.43   0.017     1.028606    10.34626
                lnrice_Post |  -3.045239   1.318348    -2.31   0.024    -5.672703   -.4177746
                 lnpop_Post |   .2015364   .3279308     0.61   0.541    -.4520287    .8551015
                lnarea_Post |   .7573249   .3352931     2.26   0.027     .0890867    1.425563
                      _cons |  -3.918913   4.774256    -0.82   0.414    -13.43399    5.596166
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B2.doc, keep(Zeng_all0_invdist_pc_yr* )   se  bdec(3) rdec(3) nocons add
> text(Observations, `e(N_full)') noobs  append 
Results\Appendix_Table_B2.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_invdist_pc, replace)
file Results\Placebo_Yearly_ctrl_all0_invdist_pc.dta saved

. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** per capita unweighted
. 
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_pc_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_
> Post  dist_nanjing_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     , ab
> sorb(year cntyid  prefidXyear)   cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       4.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6720
                                                  Adj R-squared   =     0.5453
                                                  Within R-sq.    =     0.1215
Number of clusters (cntyid)  =         74         Root MSE        =     1.1135

                                       (Std. Err. adjusted for 74 clusters in cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
          lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
Zeng_all0_pc_yr1850 |   .0067944   .0125104     0.54   0.589    -.0181388    .0317276
Zeng_all0_pc_yr1851 |  -.0003576   .0109527    -0.03   0.974    -.0221863    .0214711
Zeng_all0_pc_yr1852 |   .0067134   .0233702     0.29   0.775    -.0398633    .0532901
Zeng_all0_pc_yr1854 |   .0703474   .0236058     2.98   0.004     .0233011    .1173938
Zeng_all0_pc_yr1855 |   .0512186   .0254553     2.01   0.048     .0004862    .1019509
Zeng_all0_pc_yr1856 |   .0329745   .0291646     1.13   0.262    -.0251505    .0910996
Zeng_all0_pc_yr1857 |   .0717524   .0251847     2.85   0.006     .0215593    .1219454
Zeng_all0_pc_yr1858 |   .0685763   .0234164     2.93   0.005     .0219076     .115245
Zeng_all0_pc_yr1859 |   .0628028   .0212004     2.96   0.004     .0205504    .1050553
Zeng_all0_pc_yr1860 |   .0752207   .0271293     2.77   0.007      .021152    .1292893
Zeng_all0_pc_yr1861 |   .0654837   .0264143     2.48   0.015       .01284    .1181273
Zeng_all0_pc_yr1862 |   .0793347   .0259184     3.06   0.003     .0276794    .1309901
Zeng_all0_pc_yr1863 |   .0749637   .0273777     2.74   0.008        .0204    .1295274
Zeng_all0_pc_yr1864 |   .0711389   .0307913     2.31   0.024      .009772    .1325059
       capital_Post |  -1.361217   .3965916    -3.43   0.001    -2.151623   -.5708107
    lnurbanpop_Post |    .438591   .1339814     3.27   0.002     .1715665    .7056156
      lnjinshi_Post |   .3472428   .1458756     2.38   0.020     .0565131    .6379725
      lnquotas_Post |  -1.165929   .5521971    -2.11   0.038    -2.266456   -.0654013
        route1_Post |  -.0483474   .2794566    -0.17   0.863    -.6053036    .5086088
  dist_nanjing_Post |  -1.211584   .9834865    -1.23   0.222    -3.171669    .7485022
       mainriv_Post |  -.6528719   .2727896    -2.39   0.019    -1.196541    -.109203
    dist2canal_Post |   .4237847   1.056128     0.40   0.689    -1.681075    2.528644
       lnwheat_Post |    5.39865   2.294817     2.35   0.021     .8250873    9.972213
        lnrice_Post |  -3.060719   1.265266    -2.42   0.018    -5.582391    -.539048
         lnpop_Post |   .1889792   .3165519     0.60   0.552    -.4419078    .8198662
        lnarea_Post |   .8476787   .3290088     2.58   0.012      .191965    1.503392
              _cons |  -3.801047   4.718312    -0.81   0.423    -13.20463    5.602535
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B2.doc, keep(Zeng_all0_pc_yr* )   se  bdec(3) rdec(3) nocons addtext(Obs
> ervations, `e(N_full)') noobs  append 
Results\Appendix_Table_B2.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_pc, replace)
file Results\Placebo_Yearly_ctrl_all0_pc.dta saved

. 
. 
end of do-file

. 
. 
. ******* Table 3. The Impact of Elite Connections on Soldier Deaths: Types of Links
. 
. 
. do Programs\Table_3.do

. 
. ********************************************************************************
. ***** Table 3: The Impact of Elite Connections on Soldier Deaths: Types of Links 
. ***** Sample: Hunan counties, 1850–1864
. ********************************************************************************
.  
.  
. **This part produces Table 3 column (1)-(4)
.  
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
.  
. ********************************************* Types of networks with Pref X Year FE
. 
. 
. reghdfe  lnmartyr1  Zenghu_all_invdist_Post  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route
> 1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   , 
> absorb(year cntyid  prefidXyear)  cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =      10.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6713
                                                  Adj R-squared   =     0.5516
                                                  Within R-sq.    =     0.1196
Number of clusters (cntyid)  =         74         Root MSE        =     1.1057

                                           (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zenghu_all_invdist_Post |   .1828075   .0382995     4.77   0.000     .1064768    .2591383
           capital_Post |  -1.142686   .2824125    -4.05   0.000    -1.705534   -.5798391
        lnurbanpop_Post |    .390483   .1150002     3.40   0.001     .1612879     .619678
          lnjinshi_Post |   .3619015   .1488401     2.43   0.017     .0652636    .6585393
          lnquotas_Post |  -.7246754   .4868448    -1.49   0.141    -1.694956    .2456049
            route1_Post |  -.0944075   .2462154    -0.38   0.703    -.5851142    .3962991
      dist_nanjing_Post |  -1.709244   .9040131    -1.89   0.063    -3.510939    .0924519
           mainriv_Post |  -.6711615   .2488595    -2.70   0.009    -1.167138   -.1751851
        dist2canal_Post |   .7301576   .9426872     0.77   0.441    -1.148615     2.60893
           lnwheat_Post |   4.926226   2.360812     2.09   0.040     .2211357    9.631317
            lnrice_Post |  -4.218694   1.379822    -3.06   0.003    -6.968676   -1.468712
             lnpop_Post |  -.1940371   .3238857    -0.60   0.551    -.8395403    .4514661
            lnarea_Post |   .8041692   .3022374     2.66   0.010     .2018109    1.406528
                  _cons |   2.494381   4.637857     0.54   0.592    -6.748855    11.73762
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep(Zenghu_all_invdist_Post Zeng_BMF_invdist_Post Zeng_Juren_invdist_Post
>  Zeng_exam0_invdist_Post)    se  bdec(3) rdec(3) nocons replace
Results\Table_3.doc
dir : seeout

. 
. 
. ********
. reghdfe  lnmartyr1    Zeng_BMF_invdist_Post   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post rout
> e1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post    
> , absorb(year cntyid  prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =      10.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6675
                                                  Adj R-squared   =     0.5464
                                                  Within R-sq.    =     0.1093
Number of clusters (cntyid)  =         74         Root MSE        =     1.1122

                                         (Std. Err. adjusted for 74 clusters in cntyid)
---------------------------------------------------------------------------------------
                      |               Robust
            lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
Zeng_BMF_invdist_Post |   .1780187   .0432266     4.12   0.000     .0918682    .2641692
         capital_Post |  -.5264657   .2688925    -1.96   0.054    -1.062368    .0094363
      lnurbanpop_Post |   .3846288   .1181639     3.26   0.002     .1491286    .6201291
        lnjinshi_Post |   .4536105   .1481522     3.06   0.003     .1583437    .7488774
        lnquotas_Post |  -.8116654   .4787729    -1.70   0.094    -1.765858    .1425275
          route1_Post |  -.1171121   .3075391    -0.38   0.704    -.7300365    .4958124
    dist_nanjing_Post |  -2.005703   .9173891    -2.19   0.032    -3.834057   -.1773492
         mainriv_Post |  -.6992131   .2692446    -2.60   0.011    -1.235817   -.1626094
      dist2canal_Post |   1.007276   .9540878     1.06   0.295    -.8942179     2.90877
         lnwheat_Post |   4.246105   2.480554     1.71   0.091    -.6976319    9.189843
          lnrice_Post |  -4.425158   1.429437    -3.10   0.003    -7.274021   -1.576295
           lnpop_Post |  -.2640711   .3493598    -0.76   0.452    -.9603442     .432202
          lnarea_Post |   .7005448    .310849     2.25   0.027     .0810237    1.320066
                _cons |   5.044767   5.252847     0.96   0.340    -5.424143    15.51368
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep( Zenghu_all_invdist_Post Zeng_BMF_invdist_Post Zeng_Juren_invdist_Pos
> t Zeng_exam0_invdist_Post)    se  bdec(3) rdec(3) nocons append 
Results\Table_3.doc
dir : seeout

. 
. 
. ********
. reghdfe  lnmartyr1     Zeng_Juren_invdist_Post   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post r
> oute1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post 
>    , absorb(year cntyid  prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       4.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6637
                                                  Adj R-squared   =     0.5413
                                                  Within R-sq.    =     0.0993
Number of clusters (cntyid)  =         74         Root MSE        =     1.1184

                                           (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zeng_Juren_invdist_Post |    .199776   .0954788     2.09   0.040     .0094871    .3900649
           capital_Post |  -1.136951   .3730501    -3.05   0.003    -1.880439   -.3934635
        lnurbanpop_Post |    .438577   .1543004     2.84   0.006     .1310568    .7460973
          lnjinshi_Post |   .5013079   .1511783     3.32   0.001     .2000101    .8026058
          lnquotas_Post |   -1.05788   .5868933    -1.80   0.076    -2.227557    .1117964
            route1_Post |  -.2562188   .2834384    -0.90   0.369    -.8211107    .3086732
      dist_nanjing_Post |  -1.469058   1.058822    -1.39   0.170    -3.579288    .6411721
           mainriv_Post |  -.8110838   .3025239    -2.68   0.009    -1.414013   -.2081546
        dist2canal_Post |   .7214602   1.178948     0.61   0.542    -1.628181    3.071101
           lnwheat_Post |     4.9034   2.488972     1.97   0.053    -.0571132    9.863913
            lnrice_Post |  -4.184249    1.46733    -2.85   0.006    -7.108633   -1.259865
             lnpop_Post |  -.0301156    .368132    -0.08   0.935    -.7638016    .7035704
            lnarea_Post |   .7715821   .3501837     2.20   0.031     .0736669    1.469497
                  _cons |   .1065725   5.160575     0.02   0.984    -10.17844    10.39158
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep( Zenghu_all_invdist_Post Zeng_BMF_invdist_Post Zeng_Juren_invdist_Pos
> t Zeng_exam0_invdist_Post)    se  bdec(3) rdec(3) nocons append 
Results\Table_3.doc
dir : seeout

. 
. 
. ********
. reghdfe  lnmartyr1     Zeng_exam0_invdist_Post   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post r
> oute1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post 
>     , absorb(year cntyid  prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       4.85
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6653
                                                  Adj R-squared   =     0.5434
                                                  Within R-sq.    =     0.1034
Number of clusters (cntyid)  =         74         Root MSE        =     1.1158

                                           (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zeng_exam0_invdist_Post |    .679825   .2821682     2.41   0.019     .1174646    1.242185
           capital_Post |  -1.162575   .3846284    -3.02   0.003    -1.929138   -.3960116
        lnurbanpop_Post |   .4867348   .1427245     3.41   0.001     .2022851    .7711844
          lnjinshi_Post |   .3522903   .1689196     2.09   0.041     .0156341    .6889465
          lnquotas_Post |  -1.226014   .5643142    -2.17   0.033     -2.35069   -.1013372
            route1_Post |  -.2443478   .2799546    -0.87   0.386    -.8022965     .313601
      dist_nanjing_Post |  -1.950381   .9758933    -2.00   0.049    -3.895334   -.0054289
           mainriv_Post |   -.684121   .3013995    -2.27   0.026    -1.284809   -.0834328
        dist2canal_Post |   1.109773   1.019352     1.09   0.280    -.9217912    3.141338
           lnwheat_Post |   4.271848   2.475279     1.73   0.089    -.6613759    9.205071
            lnrice_Post |  -3.426667   1.577187    -2.17   0.033    -6.569995    -.283338
             lnpop_Post |  -.0528908   .3714669    -0.14   0.887    -.7932232    .6874416
            lnarea_Post |   .9718742   .3438628     2.83   0.006     .2865565    1.657192
                  _cons |  -.3488291   5.342554    -0.07   0.948    -10.99652    10.29886
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep( Zenghu_all_invdist_Post Zeng_BMF_invdist_Post Zeng_Juren_invdist_Pos
> t Zeng_exam0_invdist_Post)    se  bdec(3) rdec(3) nocons append 
Results\Table_3.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. 
. 
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
. ******************************* This part produces Table 3 column (5)-(7)
. 
. 
. use Data\HunanSurname.dta,clear

. 
. 
.  
. ********************** ********************** ********************** **********
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(297,000 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(297,000 real changes made)

. 
. **
. foreach y of varlist  sur_invdis_zeng_all0  capital  lnurbanpop  lnjinshi  lnquotas route1  dist_nanjing main
> riv dist2canal  lnwheat lnrice  lnarea  lnhh  {
  2. gen `y'_Post=`y'*Post
  3. }

. 
. 
. 
. ***************************************** gen FE effect
. 
. 
. *****
. 
. egen  prefXyear=group(prefid year)

. 
. gen cntyXyear=cntyid*100+(year-1850)

. gen surXyear=surname_id*100+(year-1850)

. gen cntyXsur=cntyid*1000+surname_id

. 
. 
. **************************************** gen subsample when either connected surname or martyr surname does n
> ot equal to 0
. 
. sort cntyXsur

. by cntyXsur: egen mean_sur_invdis_zeng_all0=mean(sur_invdis_zeng_all0)

. by cntyXsur: egen mean_martyr_surname=mean(martyr_surname)

. 
. gen subsample=(mean_sur_invdis_zeng_all0!=0|mean_martyr_surname!=0)

. gen subsample_network=(mean_sur_invdis_zeng_all0!=0)

. gen subsample_martyr=(mean_martyr_surname!=0)

. 
. 
. 
. *************************************** gen diff.-Surname baseline connections
. sort cntyid year

. by cntyid year: egen sum_sur_invdis_zeng_all0=sum(sur_invdis_zeng_all0)

. gen oth_sur_invdis_zeng_all0=sum_sur_invdis_zeng_all0-sur_invdis_zeng_all0

. 
. gen oth_sur_invdis_zeng_all0_Post=oth_sur_invdis_zeng_all0*Post

. 
. 
. *****************************************
. ***************************************** Regressions
. 
. 
. reghdfe  lnmartyr_surname1 sur_invdis_zeng_all0_Post ///
> lnjinshi_Post lnquotas_Post capital_Post  lnurbanpop_Post route1_Post dist_nanjing_Post mainriv_Post lnhh_Pos
> t lnarea_Post  lnwheat_Post lnrice_Post dist2canal_Post if subsample==1, ///
> absorb(prefXyear surXyear cntyXsur cntyid year  ) cluster(cntyid surname_id)
(dropped 1185 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =     48,495
Absorbing 5 HDFE groups                           F(  13,     74) =       2.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0030
                                                  R-squared       =     0.4725
                                                  Adj R-squared   =     0.3823
Number of clusters (cntyid)  =         75         Within R-sq.    =     0.0249
Number of clusters (surname_id) =        236      Root MSE        =     0.3955

                                  (Std. Err. adjusted for 75 clusters in cntyid surname_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        lnmartyr_surname1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
sur_invdis_zeng_all0_Post |    .223147   .0666069     3.35   0.001     .0904299    .3558641
            lnjinshi_Post |   .0730303    .025384     2.88   0.005     .0224516     .123609
            lnquotas_Post |  -.2654198   .1507036    -1.76   0.082    -.5657033    .0348637
             capital_Post |  -.0475044   .0843697    -0.56   0.575    -.2156148    .1206059
          lnurbanpop_Post |   .0575431   .0405693     1.42   0.160    -.0232929    .1383791
              route1_Post |  -.1271165   .0640786    -1.98   0.051    -.2547958    .0005628
        dist_nanjing_Post |  -.0084476   .2411246    -0.04   0.972    -.4888989    .4720036
             mainriv_Post |  -.2128832   .0752732    -2.83   0.006    -.3628682   -.0628981
                lnhh_Post |   .0552124   .1167511     0.47   0.638    -.1774193    .2878441
              lnarea_Post |   .1848649   .0934993     1.98   0.052    -.0014365    .3711663
             lnwheat_Post |   .4148243    .432557     0.96   0.341    -.4470644    1.276713
              lnrice_Post |  -.3740348   .2446254    -1.53   0.131    -.8614616     .113392
          dist2canal_Post |   .0217952   .2750427     0.08   0.937    -.5262393    .5698297
                    _cons |  -1.302237   1.321611    -0.99   0.328    -3.935605    1.331132
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   prefXyear |       225           0         225     |
    surXyear |      3540        3540           0    *|
    cntyXsur |      3233        3233           0    *|
      cntyid |        75          75           0    *|
        year |        15          15           0     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep(sur_invdis_zeng_all0_Post)   se  bdec(3) rdec(3) nocons append
Results\Table_3.doc
dir : seeout

. 
. 
. reghdfe  lnmartyr_surname1 sur_invdis_zeng_all0_Post oth_sur_invdis_zeng_all0_Post  ///
> lnjinshi_Post lnquotas_Post capital_Post  lnurbanpop_Post route1_Post dist_nanjing_Post mainriv_Post  lnhh_Po
> st lnarea_Post lnwheat_Post lnrice_Post dist2canal_Post if subsample==1, ///
> absorb(prefXyear surXyear cntyXsur cntyid year  ) cluster(cntyid surname_id)
(dropped 1185 singleton observations)
(MWFE estimator converged in 6 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =     48,495
Absorbing 5 HDFE groups                           F(  14,     74) =       3.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0009
                                                  R-squared       =     0.4777
                                                  Adj R-squared   =     0.3883
Number of clusters (cntyid)  =         75         Within R-sq.    =     0.0344
Number of clusters (surname_id) =        236      Root MSE        =     0.3935

                                      (Std. Err. adjusted for 75 clusters in cntyid surname_id)
-----------------------------------------------------------------------------------------------
                              |               Robust
            lnmartyr_surname1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
    sur_invdis_zeng_all0_Post |   .2485679   .0725746     3.42   0.001     .1039599     .393176
oth_sur_invdis_zeng_all0_Post |   .0564662   .0165433     3.41   0.001     .0235028    .0894295
                lnjinshi_Post |   .0245745   .0228173     1.08   0.285      -.02089    .0700389
                lnquotas_Post |  -.2026013   .1247717    -1.62   0.109    -.4512144    .0460118
                 capital_Post |  -.2832241   .1123312    -2.52   0.014    -.5070489   -.0593992
              lnurbanpop_Post |   .0676897    .034903     1.94   0.056     -.001856    .1372354
                  route1_Post |  -.0900228   .0473727    -1.90   0.061     -.184415    .0043694
            dist_nanjing_Post |   .1157576   .1940175     0.60   0.553    -.2708307    .5023458
                 mainriv_Post |  -.1404295   .0500802    -2.80   0.006    -.2402166   -.0406425
                    lnhh_Post |   .0446897   .0895715     0.50   0.619    -.1337854    .2231648
                  lnarea_Post |   .2495537   .0845813     2.95   0.004     .0810217    .4180857
                 lnwheat_Post |    .556628   .4076713     1.37   0.176    -.2556748    1.368931
                  lnrice_Post |  -.3007747   .2501693    -1.20   0.233    -.7992479    .1976986
              dist2canal_Post |  -.1237586   .2182914    -0.57   0.572    -.5587137    .3111965
                        _cons |  -1.865432   1.080179    -1.73   0.088    -4.017735    .2868716
-----------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   prefXyear |       225           0         225     |
    surXyear |      3540        3540           0    *|
    cntyXsur |      3233        3233           0    *|
      cntyid |        75          75           0    *|
        year |        15          15           0     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep(sur_invdis_zeng_all0_Post oth_sur_invdis_zeng_all0_Post)   se  bdec(3
> ) rdec(3) nocons append
Results\Table_3.doc
dir : seeout

. 
. 
. reghdfe  lnmartyr_surname1 sur_invdis_zeng_all0_Post if subsample==1, ///
> absorb(cntyXyear surXyear cntyXsur cntyid year) cluster(cntyid surname_id)
(dropped 1200 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     48,480
Absorbing 5 HDFE groups                           F(   1,     73) =      12.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0008
                                                  R-squared       =     0.6156
                                                  Adj R-squared   =     0.5400
Number of clusters (cntyid)  =         74         Within R-sq.    =     0.0027
Number of clusters (surname_id) =        236      Root MSE        =     0.3413

                                  (Std. Err. adjusted for 74 clusters in cntyid surname_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        lnmartyr_surname1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
sur_invdis_zeng_all0_Post |   .2139264   .0612877     3.49   0.001     .0917803    .3360725
                    _cons |   .1711219   .0012353   138.52   0.000     .1686599    .1735839
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   cntyXyear |      1110        1110           0    *|
    surXyear |      3540        3540           0    *|
    cntyXsur |      3232        3232           0    *|
      cntyid |        74          74           0    *|
        year |        15           0          15     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_3.doc, keep(sur_invdis_zeng_all0_Post)   se  bdec(3) rdec(3) nocons append
Results\Table_3.doc
dir : seeout

. 
. 
. 
. 
. 
end of do-file

. 
. 
. ******* Table 4. The Impact of Elite Connections on Soldier Deaths: Placebo Networks
. 
. 
. do Programs\Table_4.do

. 
. 
. ************************************************************************************************
. ******************************** Table 4: Placebo tests: change the year of exam_column(1)-(6)
. ************************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist invdist0_L1 invdist0_F1 lnarea capital lnurbanp
> op  lnpop  dist_nanjing lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. ************************************* Change the year of exam
. 
. reghdfe   lnmartyr1 Zeng_exam0_invdist_Post invdist0_L1_Post    capital_Post lnurbanpop_Post  lnjinshi_Post  
> lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Po
> st lnarea_Post    if cntyid!=25 , absorb(year cntyid prefidXyear) cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,095
Absorbing 3 HDFE groups                           F(  14,     72) =       5.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6376
                                                  Adj R-squared   =     0.5032
                                                  Within R-sq.    =     0.1023
Number of clusters (cntyid)  =         73         Root MSE        =     1.1069

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zeng_exam0_invdist_Post |   .7254692   .3161105     2.29   0.025     .0953145    1.355624
       invdist0_L1_Post |   -.027685   .2535065    -0.11   0.913     -.533041     .477671
           capital_Post |  -1.075779    .327317    -3.29   0.002    -1.728273   -.4232843
        lnurbanpop_Post |   .4546662     .12259     3.71   0.000     .2102874    .6990449
          lnjinshi_Post |   .2836652   .1581291     1.79   0.077    -.0315594    .5988898
          lnquotas_Post |  -.9324825   .4812622    -1.94   0.057    -1.891861    .0268961
            route1_Post |  -.1286827   .2679366    -0.48   0.632    -.6628046    .4054392
      dist_nanjing_Post |  -2.029698    .863906    -2.35   0.022    -3.751863    -.307533
           mainriv_Post |  -.5593985   .2749501    -2.03   0.046    -1.107501   -.0112955
        dist2canal_Post |   1.007623   .8509024     1.18   0.240    -.6886196    2.703866
           lnwheat_Post |   4.099527   2.433991     1.68   0.096    -.7525452    8.951599
            lnrice_Post |  -3.621419   1.538627    -2.35   0.021    -6.688617   -.5542211
             lnpop_Post |  -.2373446   .3337748    -0.71   0.479    -.9027125    .4280233
            lnarea_Post |   .9354945   .3286159     2.85   0.006     .2804107    1.590578
                  _cons |   2.693167   4.643809     0.58   0.564    -6.564097    11.95043
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invdi
> st0_F1_Post)    se  bdec(3) rdec(3) nocons replace 
Results\Table_4.doc
dir : seeout

. 
. 
. reghdfe   lnmartyr1  Zeng_exam0_invdist_Post invdist0_F1_Post     capital_Post lnurbanpop_Post  lnjinshi_Post
>   lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_
> Post lnarea_Post    if cntyid!=25 , absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,095
Absorbing 3 HDFE groups                           F(  14,     72) =       5.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6377
                                                  Adj R-squared   =     0.5033
                                                  Within R-sq.    =     0.1025
Number of clusters (cntyid)  =         73         Root MSE        =     1.1068

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zeng_exam0_invdist_Post |   .7556479   .3346934     2.26   0.027     .0884489    1.422847
       invdist0_F1_Post |  -.0847875   .1922554    -0.44   0.661    -.4680417    .2984667
           capital_Post |  -1.069164   .3240714    -3.30   0.002    -1.715189   -.4231398
        lnurbanpop_Post |   .4578763   .1208297     3.79   0.000     .2170067    .6987459
          lnjinshi_Post |   .2777616   .1551885     1.79   0.078    -.0316011    .5871242
          lnquotas_Post |  -.9065204   .4823362    -1.88   0.064     -1.86804    .0549992
            route1_Post |  -.1587107   .2856996    -0.56   0.580    -.7282423     .410821
      dist_nanjing_Post |  -2.041551   .8525391    -2.39   0.019    -3.741056   -.3420451
           mainriv_Post |  -.5525182   .2769083    -2.00   0.050    -1.104525   -.0005115
        dist2canal_Post |   1.006626   .8401362     1.20   0.235    -.6681548    2.681407
           lnwheat_Post |   4.249617   2.466519     1.72   0.089    -.6672993    9.166534
            lnrice_Post |  -3.673848   1.550049    -2.37   0.020    -6.763814   -.5838814
             lnpop_Post |  -.2505182   .3393409    -0.74   0.463     -.926982    .4259456
            lnarea_Post |   .9241814   .3228183     2.86   0.005      .280655    1.567708
                  _cons |     2.7755    4.64069     0.60   0.552    -6.475547    12.02655
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invdi
> st0_F1_Post)    se  bdec(3) rdec(3) nocons append
Results\Table_4.doc
dir : seeout

. 
. 
. reghdfe   lnmartyr1 Zeng_exam0_invdist_Post invdist0_L1_Post  invdist0_F1_Post   capital_Post lnurbanpop_Post
>   lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnr
> ice_Post lnpop_Post lnarea_Post    if cntyid!=25 , absorb(year cntyid prefidXyear) cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,095
Absorbing 3 HDFE groups                           F(  15,     72) =       5.93
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6378
                                                  Adj R-squared   =     0.5028
                                                  Within R-sq.    =     0.1026
Number of clusters (cntyid)  =         73         Root MSE        =     1.1074

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
              lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
Zeng_exam0_invdist_Post |   .7271505   .3164152     2.30   0.024     .0963883    1.357913
       invdist0_L1_Post |   .1459688   .5374542     0.27   0.787    -.9254265    1.217364
       invdist0_F1_Post |  -.1787822   .4223953    -0.42   0.673    -1.020812    .6632474
           capital_Post |  -1.050858    .324173    -3.24   0.002    -1.697085   -.4046311
        lnurbanpop_Post |   .4369963   .1249957     3.50   0.001     .1878219    .6861707
          lnjinshi_Post |   .2566239   .1716899     1.49   0.139    -.0856337    .5988815
          lnquotas_Post |  -.8451061   .5031717    -1.68   0.097    -1.848161    .1579484
            route1_Post |  -.1758842   .2983364    -0.59   0.557     -.770607    .4188387
      dist_nanjing_Post |  -2.024959   .8621642    -2.35   0.022    -3.743652   -.3062665
           mainriv_Post |  -.5484589   .2746623    -2.00   0.050    -1.095988   -.0009296
        dist2canal_Post |   .9896739   .8491006     1.17   0.248    -.7029773    2.682325
           lnwheat_Post |   4.369981   2.409493     1.81   0.074    -.4332557    9.173217
            lnrice_Post |  -3.720744   1.549756    -2.40   0.019    -6.810126   -.6313622
             lnpop_Post |  -.2579344   .3413357    -0.76   0.452    -.9383748    .4225059
            lnarea_Post |   .9184586   .3168753     2.90   0.005     .2867793    1.550138
                  _cons |   2.806085   4.639113     0.60   0.547    -6.441818    12.05399
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invdi
> st0_F1_Post)    se  bdec(3) rdec(3) nocons append
Results\Table_4.doc
dir : seeout

. 
. 
. 
. 
. ************************************* IV estimates
. 
. 
.  
. ivreghdfe lnmartyr1   (Zeng_all0_invdist_Post=Zeng_exam0_invdist_Post) invdist0_L1_Post     capital_Post lnur
> banpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwhe
> at_Post lnrice_Post lnpop_Post lnarea_Post  if cntyid!=25 ,  absorb(year cntyid  prefidXyear) cluster(  cntyi
> d)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cntyid

Number of clusters (cntyid) =       73                Number of obs =     1095
                                                      F( 14,    72) =     4.67
                                                      Prob > F      =   0.0000
Total (centered) SS     =  1089.140779                Centered R2   =   0.1016
Total (uncentered) SS   =  1089.140779                Uncentered R2 =   0.1016
Residual SS             =  978.5359152                Root MSE      =     1.06

----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .3289626   .1374985     2.39   0.019     .0548643    .6030609
      invdist0_L1_Post |  -.3027153   .3285327    -0.92   0.360    -.9576333    .3522026
          capital_Post |  -1.485894   .5060098    -2.94   0.004    -2.494606   -.4771819
       lnurbanpop_Post |   .4657297   .1376487     3.38   0.001     .1913321    .7401273
         lnjinshi_Post |    .402491   .1601161     2.51   0.014     .0833053    .7216767
         lnquotas_Post |  -.7461715   .5148852    -1.45   0.152    -1.772577    .2802334
           route1_Post |  -.1570021   .2621786    -0.60   0.551    -.6796456    .3656414
     dist_nanjing_Post |  -1.357079    .971753    -1.40   0.167    -3.294234    .5800748
          mainriv_Post |  -.6492973   .2698487    -2.41   0.019    -1.187231   -.1113637
       dist2canal_Post |   .3974688    1.03952     0.38   0.703    -1.674776    2.469714
          lnwheat_Post |   5.258269   2.471099     2.13   0.037     .3322238    10.18432
           lnrice_Post |  -4.325085   1.461332    -2.96   0.004    -7.238198   -1.411972
            lnpop_Post |  -.2033696   .3229114    -0.63   0.531    -.8470818    .4403426
           lnarea_Post |   .7766796   .3220615     2.41   0.018     .1346617    1.418697
----------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.470
                                                   Chi-sq(1) P-val =    0.0193
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              760.808
                         (Kleibergen-Paap rk Wald F statistic):         11.670
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         Zeng_all0_invdist_Post
Included instruments: invdist0_L1_Post capital_Post lnurbanpop_Post
                      lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post
                      mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post
                      lnpop_Post lnarea_Post
Excluded instruments: Zeng_exam0_invdist_Post
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.  outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invd
> ist0_F1_Post)    se  bdec(3) rdec(3) nocons append
Results\Table_4.doc
dir : seeout

. 
.  
. ivreghdfe  lnmartyr1   (Zeng_all0_invdist_Post=Zeng_exam0_invdist_Post)   invdist0_F1_Post  capital_Post lnur
> banpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwhe
> at_Post lnrice_Post lnpop_Post lnarea_Post  if cntyid!=25 ,  absorb(year cntyid  prefidXyear) cluster(  cntyi
> d)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cntyid

Number of clusters (cntyid) =       73                Number of obs =     1095
                                                      F( 14,    72) =     5.47
                                                      Prob > F      =   0.0000
Total (centered) SS     =  1089.140779                Centered R2   =   0.1022
Total (uncentered) SS   =  1089.140779                Uncentered R2 =   0.1022
Residual SS             =  977.8414948                Root MSE      =     1.06

----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .3233732   .1397575     2.31   0.024     .0447718    .6019747
      invdist0_F1_Post |  -.2782189   .2525312    -1.10   0.274    -.7816306    .2251929
          capital_Post |  -1.437498   .4766283    -3.02   0.004    -2.387639   -.4873569
       lnurbanpop_Post |   .4321496   .1305024     3.31   0.001     .1719979    .6923013
         lnjinshi_Post |    .354871   .1497407     2.37   0.020     .0563684    .6533736
         lnquotas_Post |   -.605492   .5351424    -1.13   0.262    -1.672279    .4612948
           route1_Post |  -.2260884   .2788155    -0.81   0.420    -.7818969      .32972
     dist_nanjing_Post |  -1.353962   .9608869    -1.41   0.163    -3.269455    .5615306
          mainriv_Post |  -.6316064   .2714547    -2.33   0.023    -1.172741   -.0904714
       dist2canal_Post |   .3760809   1.025316     0.37   0.715    -1.667849     2.42001
          lnwheat_Post |   5.648357   2.570857     2.20   0.031     .5234466    10.77327
           lnrice_Post |   -4.46499   1.483562    -3.01   0.004    -7.422418   -1.507562
            lnpop_Post |  -.2342548    .321003    -0.73   0.468    -.8741626    .4056529
           lnarea_Post |   .7545115   .3109105     2.43   0.018     .1347228      1.3743
----------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              3.631
                                                   Chi-sq(1) P-val =    0.0567
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              933.196
                         (Kleibergen-Paap rk Wald F statistic):          9.889
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         Zeng_all0_invdist_Post
Included instruments: invdist0_F1_Post capital_Post lnurbanpop_Post
                      lnjinshi_Post lnquotas_Post route1_Post dist_nanjing_Post
                      mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post
                      lnpop_Post lnarea_Post
Excluded instruments: Zeng_exam0_invdist_Post
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invdi
> st0_F1_Post)    se  bdec(3) rdec(3) nocons append
Results\Table_4.doc
dir : seeout

. 
.  
. ivreghdfe  lnmartyr1   (Zeng_all0_invdist_Post=Zeng_exam0_invdist_Post)  invdist0_L1_Post   invdist0_F1_Post 
> capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2c
> anal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  if cntyid!=25 ,  absorb(year cntyid  prefidXyear
> ) cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cntyid

Number of clusters (cntyid) =       73                Number of obs =     1095
                                                      F( 15,    72) =     6.09
                                                      Prob > F      =   0.0000
Total (centered) SS     =  1089.140779                Centered R2   =   0.1020
Total (uncentered) SS   =  1089.140779                Uncentered R2 =   0.1020
Residual SS             =  978.0399732                Root MSE      =     1.06

----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .3299496   .1427476     2.31   0.024     .0453874    .6145118
      invdist0_L1_Post |  -.0787151   .5350046    -0.15   0.883    -1.145227    .9877971
      invdist0_F1_Post |  -.2314651   .4076903    -0.57   0.572    -1.044181    .5812506
          capital_Post |  -1.454861   .4951356    -2.94   0.004    -2.441895   -.4678257
       lnurbanpop_Post |   .4428862    .134959     3.28   0.002     .1738504     .711922
         lnjinshi_Post |   .3678378   .1695706     2.17   0.033      .029805    .7058707
         lnquotas_Post |  -.6324883   .5332134    -1.19   0.239     -1.69543    .4304533
           route1_Post |  -.2181977   .2911392    -0.75   0.456    -.7985731    .3621776
     dist_nanjing_Post |  -1.348926   .9586514    -1.41   0.164    -3.259963    .5621105
          mainriv_Post |  -.6354038   .2634163    -2.41   0.018    -1.160515    -.110293
       dist2canal_Post |   .3723993   1.028224     0.36   0.718    -1.677328    2.422127
          lnwheat_Post |   5.611896   2.584138     2.17   0.033     .4605109    10.76328
           lnrice_Post |   -4.45579   1.496972    -2.98   0.004    -7.439949   -1.471632
            lnpop_Post |  -.2299248   .3247602    -0.71   0.481    -.8773225    .4174729
           lnarea_Post |   .7541471    .313437     2.41   0.019     .1293218    1.378972
----------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.194
                                                   Chi-sq(1) P-val =    0.0227
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              760.615
                         (Kleibergen-Paap rk Wald F statistic):         11.060
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         Zeng_all0_invdist_Post
Included instruments: invdist0_L1_Post invdist0_F1_Post capital_Post
                      lnurbanpop_Post lnjinshi_Post lnquotas_Post route1_Post
                      dist_nanjing_Post mainriv_Post dist2canal_Post
                      lnwheat_Post lnrice_Post lnpop_Post lnarea_Post
Excluded instruments: Zeng_exam0_invdist_Post
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        73          73           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post Zeng_exam0_invdist_Post invdist0_L1_Post invdi
> st0_F1_Post)    se  bdec(3) rdec(3) nocons append
Results\Table_4.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. 
. 
. ************************************************************************************************
. ************************************************************************************************
. ******************************** Table 4: Placebo tests: change the year of exam_column(7)-(11)
. ************************************************************************************************
. ************************************************************************************************
. 
. 
. use Data\HuaiYr.dta,clear

. 
. 
. 
. ****************************
. 
. 
. gen Post=0 if year<1854
(1,463 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(1,463 real changes made)

. 
. 
. *********
. foreach y of varlist  Zeng_all0_invdist   Zeng_exam0_invdist invdist0_L1 invdist0_F1  lncntyarea lncntypop   
>  lnrice lnwheat  mainriv dist2canal   prefcap     lnurbanpop   lnjinshi  lncntyquota0   dist_nanjing   Taipin
> g_route1 {
  2. gen `y'_Post=`y'*Post
  3. }

.  
.   
. 
. ********************* Table 4, columns (7)-(11)
. 
. xi: reghdfe  lnmartyr_yr  prefcap_Post  lnurbanpop_Post  lnjinshi_Post  lncntyquota0_Post  Taiping_route1_Pos
> t dist_nanjing_Post mainriv_Post dist2canal_Post    lnrice_Post lnwheat_Post lncntypop_Post lncntyarea_Post  
> Zeng_all0_invdist_Post , absorb(year samcntyid   prefidXyear)  cluster(  samcntyid)
(MWFE estimator converged in 2 iterations)
note: lncntypop_Post omitted because of collinearity
note: lncntyarea_Post omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,995
Absorbing 3 HDFE groups                           F(  11,    132) =       1.06
Statistics robust to heteroskedasticity           Prob > F        =     0.4001
                                                  R-squared       =     0.5606
                                                  Adj R-squared   =     0.4123
                                                  Within R-sq.    =     0.0079
Number of clusters (samcntyid) =        133       Root MSE        =     0.6670

                                      (Std. Err. adjusted for 133 clusters in samcntyid)
----------------------------------------------------------------------------------------
                       |               Robust
           lnmartyr_yr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          prefcap_Post |  -.1507444   .1031998    -1.46   0.146    -.3548838    .0533951
       lnurbanpop_Post |   .0193747   .0164863     1.18   0.242    -.0132369    .0519862
         lnjinshi_Post |  -.0415445   .0402712    -1.03   0.304     -.121205     .038116
     lncntyquota0_Post |   .1022341   .0704867     1.45   0.149    -.0371956    .2416638
   Taiping_route1_Post |  -.2345337   .2494309    -0.94   0.349    -.7279327    .2588653
     dist_nanjing_Post |  -.2216878   .1345852    -1.65   0.102    -.4879106     .044535
          mainriv_Post |  -.0758711   .1501916    -0.51   0.614     -.372965    .2212227
       dist2canal_Post |   .0481073   .1477811     0.33   0.745    -.2442184     .340433
           lnrice_Post |   .0126831   .2100612     0.06   0.952    -.4028387    .4282048
          lnwheat_Post |  -.0984097   .2285477    -0.43   0.667    -.5504996    .3536801
        lncntypop_Post |          0  (omitted)
       lncntyarea_Post |          0  (omitted)
Zeng_all0_invdist_Post |   .0291048   .0364162     0.80   0.426      -.04293    .1011397
                 _cons |   .3636794   .1982103     1.83   0.069    -.0284001    .7557589
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
   samcntyid |       133         133           0    *|
 prefidXyear |       360          15         345     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post)   se  bdec(3) rdec(3) nocons append 
Results\Table_4.doc
dir : seeout

. 
. 
. xi: reghdfe  lnmartyr_yr  prefcap_Post  lnurbanpop_Post  lnjinshi_Post  lncntyquota0_Post  Taiping_route1_Pos
> t dist_nanjing_Post mainriv_Post dist2canal_Post    lnrice_Post lnwheat_Post lncntypop_Post lncntyarea_Post  
> Zeng_exam0_invdist_Post , absorb(year samcntyid   prefidXyear)  cluster(  samcntyid)
(MWFE estimator converged in 2 iterations)
note: lncntypop_Post omitted because of collinearity
note: lncntyarea_Post omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,995
Absorbing 3 HDFE groups                           F(  11,    132) =       1.08
Statistics robust to heteroskedasticity           Prob > F        =     0.3845
                                                  R-squared       =     0.5607
                                                  Adj R-squared   =     0.4125
                                                  Within R-sq.    =     0.0083
Number of clusters (samcntyid) =        133       Root MSE        =     0.6669

                                       (Std. Err. adjusted for 133 clusters in samcntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
            lnmartyr_yr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
           prefcap_Post |  -.1541036   .1035943    -1.49   0.139    -.3590234    .0508162
        lnurbanpop_Post |   .0203028   .0164702     1.23   0.220    -.0122769    .0528825
          lnjinshi_Post |  -.0416645   .0391457    -1.06   0.289    -.1190986    .0357696
      lncntyquota0_Post |   .1087286   .0719301     1.51   0.133    -.0335563    .2510134
    Taiping_route1_Post |  -.2195573   .2522637    -0.87   0.386    -.7185598    .2794452
      dist_nanjing_Post |  -.2171278   .1351777    -1.61   0.111    -.4845227     .050267
           mainriv_Post |  -.0795487   .1507201    -0.53   0.599     -.377688    .2185906
        dist2canal_Post |   .0461326   .1474913     0.31   0.755    -.2456197    .3378848
            lnrice_Post |   .0210414   .2092216     0.10   0.920    -.3928195    .4349024
           lnwheat_Post |  -.1178602   .2284826    -0.52   0.607    -.5698213    .3341009
         lncntypop_Post |          0  (omitted)
        lncntyarea_Post |          0  (omitted)
Zeng_exam0_invdist_Post |   .0470588   .0517852     0.91   0.365    -.0553774     .149495
                  _cons |   .3524703   .1999724     1.76   0.080     -.043095    .7480355
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
   samcntyid |       133         133           0    *|
 prefidXyear |       360          15         345     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_exam0_invdist_Post)   se  bdec(3) rdec(3) nocons append   
Results\Table_4.doc
dir : seeout

. 
. 
. xi: reghdfe  lnmartyr_yr  prefcap_Post  lnurbanpop_Post  lnjinshi_Post  lncntyquota0_Post  Taiping_route1_Pos
> t dist_nanjing_Post mainriv_Post dist2canal_Post    lnrice_Post lnwheat_Post lncntypop_Post lncntyarea_Post  
> Zeng_exam0_invdist_Post invdist0_L1_Post, absorb(year samcntyid   prefidXyear)  cluster(  samcntyid)
(MWFE estimator converged in 2 iterations)
note: lncntypop_Post omitted because of collinearity
note: lncntyarea_Post omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,995
Absorbing 3 HDFE groups                           F(  12,    132) =       0.99
Statistics robust to heteroskedasticity           Prob > F        =     0.4576
                                                  R-squared       =     0.5608
                                                  Adj R-squared   =     0.4122
                                                  Within R-sq.    =     0.0084
Number of clusters (samcntyid) =        133       Root MSE        =     0.6671

                                       (Std. Err. adjusted for 133 clusters in samcntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
            lnmartyr_yr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
           prefcap_Post |  -.1567096   .1023446    -1.53   0.128    -.3591574    .0457382
        lnurbanpop_Post |   .0199596   .0166899     1.20   0.234    -.0130546    .0529738
          lnjinshi_Post |  -.0417542   .0392077    -1.06   0.289    -.1193109    .0358025
      lncntyquota0_Post |   .1016629    .075788     1.34   0.182    -.0482532    .2515791
    Taiping_route1_Post |  -.2245258   .2519415    -0.89   0.374     -.722891    .2738394
      dist_nanjing_Post |  -.2202965    .134553    -1.64   0.104    -.4864556    .0458627
           mainriv_Post |  -.0808651   .1500448    -0.54   0.591    -.3776684    .2159383
        dist2canal_Post |   .0492574   .1474302     0.33   0.739    -.2423741    .3408888
            lnrice_Post |   .0078981   .2106566     0.04   0.970    -.4088014    .4245976
           lnwheat_Post |  -.0975363   .2385515    -0.41   0.683    -.5694147    .3743421
         lncntypop_Post |          0  (omitted)
        lncntyarea_Post |          0  (omitted)
Zeng_exam0_invdist_Post |   .0295238   .0555077     0.53   0.596    -.0802759    .1393234
       invdist0_L1_Post |   .0213931   .0474195     0.45   0.653    -.0724074    .1151936
                  _cons |   .3623542   .1978642     1.83   0.069    -.0290408    .7537492
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
   samcntyid |       133         133           0    *|
 prefidXyear |       360          15         345     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post  Zeng_exam0_invdist_Post invdist0_L1_Post )   
> se  bdec(3) rdec(3) nocons append  
Results\Table_4.doc
dir : seeout

. 
. 
. xi: reghdfe  lnmartyr_yr  prefcap_Post  lnurbanpop_Post  lnjinshi_Post  lncntyquota0_Post  Taiping_route1_Pos
> t dist_nanjing_Post mainriv_Post dist2canal_Post    lnrice_Post lnwheat_Post lncntypop_Post lncntyarea_Post  
> Zeng_exam0_invdist_Post  invdist0_F1_Post , absorb(year samcntyid   prefidXyear)  cluster(  samcntyid)
(MWFE estimator converged in 2 iterations)
note: lncntypop_Post omitted because of collinearity
note: lncntyarea_Post omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,995
Absorbing 3 HDFE groups                           F(  12,    132) =       0.99
Statistics robust to heteroskedasticity           Prob > F        =     0.4646
                                                  R-squared       =     0.5607
                                                  Adj R-squared   =     0.4121
                                                  Within R-sq.    =     0.0083
Number of clusters (samcntyid) =        133       Root MSE        =     0.6671

                                       (Std. Err. adjusted for 133 clusters in samcntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
            lnmartyr_yr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
           prefcap_Post |  -.1541486   .1036763    -1.49   0.139    -.3592306    .0509335
        lnurbanpop_Post |   .0202914    .016449     1.23   0.220    -.0122464    .0528291
          lnjinshi_Post |  -.0416241   .0392539    -1.06   0.291    -.1192722    .0360239
      lncntyquota0_Post |   .1089901   .0744851     1.46   0.146    -.0383489    .2563291
    Taiping_route1_Post |  -.2190504    .254389    -0.86   0.391     -.722257    .2841561
      dist_nanjing_Post |  -.2170961   .1350494    -1.61   0.110    -.4842372    .0500449
           mainriv_Post |  -.0795837   .1508573    -0.53   0.599    -.3779943    .2188269
        dist2canal_Post |   .0459368   .1476416     0.31   0.756    -.2461129    .3379864
            lnrice_Post |   .0214805   .2106709     0.10   0.919    -.3952473    .4382083
           lnwheat_Post |  -.1185972   .2357121    -0.50   0.616     -.584859    .3476645
         lncntypop_Post |          0  (omitted)
        lncntyarea_Post |          0  (omitted)
Zeng_exam0_invdist_Post |   .0477103   .0532831     0.90   0.372    -.0576889    .1531096
       invdist0_F1_Post |  -.0011139    .050053    -0.02   0.982    -.1001236    .0978959
                  _cons |   .3524051   .1996044     1.77   0.080    -.0424322    .7472424
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
   samcntyid |       133         133           0    *|
 prefidXyear |       360          15         345     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_all0_invdist_Post  Zeng_exam0_invdist_Post invdist0_F1_Post)   s
> e  bdec(3) rdec(3) nocons append  
Results\Table_4.doc
dir : seeout

. 
. 
. xi: reghdfe  lnmartyr_yr  prefcap_Post  lnurbanpop_Post  lnjinshi_Post  lncntyquota0_Post  Taiping_route1_Pos
> t dist_nanjing_Post mainriv_Post dist2canal_Post    lnrice_Post lnwheat_Post lncntypop_Post lncntyarea_Post Z
> eng_exam0_invdist_Post invdist0_L1_Post  invdist0_F1_Post , absorb(year samcntyid   prefidXyear)  cluster(  s
> amcntyid)
(MWFE estimator converged in 2 iterations)
note: lncntypop_Post omitted because of collinearity
note: lncntyarea_Post omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,995
Absorbing 3 HDFE groups                           F(  13,    132) =       1.02
Statistics robust to heteroskedasticity           Prob > F        =     0.4348
                                                  R-squared       =     0.5610
                                                  Adj R-squared   =     0.4122
                                                  Within R-sq.    =     0.0090
Number of clusters (samcntyid) =        133       Root MSE        =     0.6671

                                       (Std. Err. adjusted for 133 clusters in samcntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
            lnmartyr_yr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
           prefcap_Post |  -.1716808   .0989389    -1.74   0.085    -.3673917      .02403
        lnurbanpop_Post |   .0174158   .0168843     1.03   0.304     -.015983    .0508146
          lnjinshi_Post |   -.037923   .0387097    -0.98   0.329    -.1144946    .0386486
      lncntyquota0_Post |   .1008158    .075608     1.33   0.185    -.0487442    .2503759
    Taiping_route1_Post |   -.191649   .2560796    -0.75   0.456    -.6981998    .3149018
      dist_nanjing_Post |  -.2295465   .1345086    -1.71   0.090    -.4956178    .0365248
           mainriv_Post |  -.0896986   .1511341    -0.59   0.554    -.3886568    .2092596
        dist2canal_Post |    .041329   .1498178     0.28   0.783    -.2550255    .3376835
            lnrice_Post |      .0014   .2080598     0.01   0.995    -.4101628    .4129629
           lnwheat_Post |  -.0935087   .2368191    -0.39   0.694    -.5619603    .3749429
         lncntypop_Post |          0  (omitted)
        lncntyarea_Post |          0  (omitted)
Zeng_exam0_invdist_Post |   .0276706   .0541719     0.51   0.610    -.0794867     .134828
       invdist0_L1_Post |    .106042   .0968038     1.10   0.275    -.0854455    .2975295
       invdist0_F1_Post |  -.1154492   .1063433    -1.09   0.280    -.3258067    .0949084
                  _cons |   .3947114   .2061388     1.91   0.058    -.0130515    .8024743
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
   samcntyid |       133         133           0    *|
 prefidXyear |       360          15         345     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_4.doc, keep(Zeng_exam0_invdist_Post  Zeng_exam0_invdist_Post invdist0_L1_Post  in
> vdist0_F1_Post)   se  bdec(3) rdec(3) nocons append  
Results\Table_4.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. 
. ******* Figure 4. The Impact of Elite Connections on Soldier Deaths: Year-by-Year Estimates
. 
. 
. do Programs\Figure_4.do

. 
. *********************************************************************************
. *** Figure 4: The Impact of Elite Connections on Soldier Deaths: Year-by-Year Estimates
. *********************************************************************************
. 
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist lnarea capital lnurbanpop  lnpop  dist_nanjing 
> lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  {
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. 
. ********************** ********************** ********************** **********************
. ********************** gen year dummies
. 
. 
. tab year, gen(year)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1850 |         75        6.67        6.67
       1851 |         75        6.67       13.33
       1852 |         75        6.67       20.00
       1853 |         75        6.67       26.67
       1854 |         75        6.67       33.33
       1855 |         75        6.67       40.00
       1856 |         75        6.67       46.67
       1857 |         75        6.67       53.33
       1858 |         75        6.67       60.00
       1859 |         75        6.67       66.67
       1860 |         75        6.67       73.33
       1861 |         75        6.67       80.00
       1862 |         75        6.67       86.67
       1863 |         75        6.67       93.33
       1864 |         75        6.67      100.00
------------+-----------------------------------
      Total |      1,125      100.00

. local r=1 

. while `r'<16 {
  2. local s=`r'+1849
  3. rename year`r' yr`s'
  4. local r=`r'+1
  5. }

. 
. **
. foreach y of varlist    Zeng_all0_invdist Zeng_all0 Zeng_all0_invdist_pc Zeng_all0_pc{
  2. foreach x of varlist yr1850-yr1852 yr1854-yr1864 {
  3. gen `y'_`x'=`y'*`x'
  4. }
  5. }

. 
. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** Weighted
. 
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_invdist_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post ro
> ute1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   
>    , absorb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       6.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6711
                                                  Adj R-squared   =     0.5440
                                                  Within R-sq.    =     0.1190
Number of clusters (cntyid)  =         74         Root MSE        =     1.1151

                                            (Std. Err. adjusted for 74 clusters in cntyid)
------------------------------------------------------------------------------------------
                         |               Robust
               lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
Zeng_all0_invdist_yr1850 |  -.0024709   .0522458    -0.05   0.962    -.1065965    .1016547
Zeng_all0_invdist_yr1851 |  -.0164508   .0494299    -0.33   0.740    -.1149646    .0820629
Zeng_all0_invdist_yr1852 |   .0297401   .0836645     0.36   0.723     -.137003    .1964831
Zeng_all0_invdist_yr1854 |   .2872017   .0876611     3.28   0.002     .1124935    .4619099
Zeng_all0_invdist_yr1855 |   .2323828   .1084022     2.14   0.035     .0163375    .4484281
Zeng_all0_invdist_yr1856 |   .1561531   .1178838     1.32   0.189    -.0787889    .3910951
Zeng_all0_invdist_yr1857 |   .2834177   .0867142     3.27   0.002     .1105966    .4562387
Zeng_all0_invdist_yr1858 |   .3125987   .0855649     3.65   0.000      .142068    .4831293
Zeng_all0_invdist_yr1859 |    .254592   .0835678     3.05   0.003     .0880416    .4211423
Zeng_all0_invdist_yr1860 |   .2401093   .0874939     2.74   0.008     .0657342    .4144844
Zeng_all0_invdist_yr1861 |   .2421287   .0804964     3.01   0.004     .0816995    .4025578
Zeng_all0_invdist_yr1862 |   .2877558   .0833906     3.45   0.001     .1215585     .453953
Zeng_all0_invdist_yr1863 |   .2960503   .0856566     3.46   0.001     .1253369    .4667637
Zeng_all0_invdist_yr1864 |   .3131157   .0861566     3.63   0.001     .1414058    .4848256
            capital_Post |  -1.432147   .4275203    -3.35   0.001    -2.284193   -.5800999
         lnurbanpop_Post |   .4336873   .1371317     3.16   0.002     .1603843    .7069903
           lnjinshi_Post |   .3889764   .1435955     2.71   0.008      .102791    .6751619
           lnquotas_Post |  -.8923757   .5377417    -1.66   0.101    -1.964093    .1793419
             route1_Post |  -.1870466   .2492757    -0.75   0.455    -.6838523    .3097591
       dist_nanjing_Post |  -1.389248   .9876837    -1.41   0.164    -3.357699    .5792024
            mainriv_Post |  -.7111534   .2727249    -2.61   0.011    -1.254693   -.1676134
         dist2canal_Post |    .551294   1.061714     0.52   0.605    -1.564698    2.667287
            lnwheat_Post |   5.029848   2.456125     2.05   0.044     .1347981    9.924898
             lnrice_Post |   -4.04189   1.455684    -2.78   0.007    -6.943064   -1.140715
              lnpop_Post |  -.0872555   .3436365    -0.25   0.800     -.772122     .597611
             lnarea_Post |   .8448603     .33093     2.55   0.013     .1853177    1.504403
                   _cons |   .1722239   4.826257     0.04   0.972    -9.446492     9.79094
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Placebo_Yearly_ctrl_all0_invdist.doc, keep(Zeng_all0_invdist_yr* )   se  bdec(3) rdec(3
> ) nocons replace 
Results\Placebo_Yearly_ctrl_all0_invdist.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_invdist, replace)
file Results\Placebo_Yearly_ctrl_all0_invdist.dta saved

. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** unweighted
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Pos
> t  dist_nanjing_Post mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     , abso
> rb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       6.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6704
                                                  Adj R-squared   =     0.5430
                                                  Within R-sq.    =     0.1170
Number of clusters (cntyid)  =         74         Root MSE        =     1.1163

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
        lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
 Zeng_all0_yr1850 |   .0098665    .033781     0.29   0.771    -.0574589    .0771919
 Zeng_all0_yr1851 |   -.001187   .0312959    -0.04   0.970    -.0635597    .0611857
 Zeng_all0_yr1852 |   .0256354   .0565717     0.45   0.652    -.0871118    .1383826
 Zeng_all0_yr1854 |   .2119564   .0616577     3.44   0.001     .0890729      .33484
 Zeng_all0_yr1855 |   .1634491   .0780445     2.09   0.040     .0079067    .3189914
 Zeng_all0_yr1856 |   .1149849   .0847825     1.36   0.179    -.0539865    .2839562
 Zeng_all0_yr1857 |   .2066915   .0614515     3.36   0.001     .0842189    .3291641
 Zeng_all0_yr1858 |   .2153759   .0608531     3.54   0.001     .0940959    .3366559
 Zeng_all0_yr1859 |   .1929583   .0575994     3.35   0.001     .0781629    .3077537
 Zeng_all0_yr1860 |   .1875302   .0604424     3.10   0.003     .0670687    .3079918
 Zeng_all0_yr1861 |     .17755   .0580123     3.06   0.003     .0619317    .2931683
 Zeng_all0_yr1862 |    .201194   .0577336     3.48   0.001     .0861311    .3162568
 Zeng_all0_yr1863 |   .2069772    .063548     3.26   0.002     .0803262    .3336282
 Zeng_all0_yr1864 |   .2162984      .0703     3.08   0.003     .0761907     .356406
     capital_Post |  -1.388029    .420896    -3.30   0.002    -2.226873   -.5491844
  lnurbanpop_Post |   .4065032    .133003     3.06   0.003     .1414287    .6715778
    lnjinshi_Post |   .3379932   .1508445     2.24   0.028     .0373605     .638626
    lnquotas_Post |  -.9130775   .5490673    -1.66   0.101    -2.007367    .1812121
      route1_Post |  -.1637036   .2531433    -0.65   0.520    -.6682176    .3408103
dist_nanjing_Post |  -1.558502   .9933188    -1.57   0.121    -3.538184    .4211794
     mainriv_Post |  -.7032625   .2730541    -2.58   0.012    -1.247458   -.1590665
  dist2canal_Post |   .7314203   1.051455     0.70   0.489    -1.364125    2.826966
     lnwheat_Post |   4.948021   2.436897     2.03   0.046     .0912926     9.80475
      lnrice_Post |  -3.901067   1.450319    -2.69   0.009    -6.791548   -1.010586
       lnpop_Post |  -.0518096   .3440628    -0.15   0.881    -.7375258    .6339066
      lnarea_Post |    .905266   .3307484     2.74   0.008     .2460853    1.564447
            _cons |  -.4145257   4.955172    -0.08   0.934    -10.29017    9.461118
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Placebo_Yearly_ctrl_all0_invdist.doc, keep(Zeng_all0_yr* )   se  bdec(3) rdec(3) nocons
>  append 
Results\Placebo_Yearly_ctrl_all0_invdist.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0, replace)
file Results\Placebo_Yearly_ctrl_all0.dta saved

. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** per capita weighted
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_invdist_pc_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post
>  route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post 
>      , absorb(year cntyid  prefidXyear)   cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       4.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6735
                                                  Adj R-squared   =     0.5474
                                                  Within R-sq.    =     0.1255
Number of clusters (cntyid)  =         74         Root MSE        =     1.1109

                                               (Std. Err. adjusted for 74 clusters in cntyid)
---------------------------------------------------------------------------------------------
                            |               Robust
                  lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
Zeng_all0_invdist_pc_yr1850 |   .0054489    .017352     0.31   0.754    -.0291336    .0400315
Zeng_all0_invdist_pc_yr1851 |  -.0058021   .0156101    -0.37   0.711    -.0369129    .0253088
Zeng_all0_invdist_pc_yr1852 |   .0032408   .0347261     0.09   0.926    -.0659682    .0724498
Zeng_all0_invdist_pc_yr1854 |   .0949567   .0321884     2.95   0.004     .0308054     .159108
Zeng_all0_invdist_pc_yr1855 |   .0697685   .0344246     2.03   0.046     .0011603    .1383766
Zeng_all0_invdist_pc_yr1856 |   .0387974   .0373764     1.04   0.303    -.0356937    .1132885
Zeng_all0_invdist_pc_yr1857 |   .0938249   .0318083     2.95   0.004     .0304311    .1572187
Zeng_all0_invdist_pc_yr1858 |   .0937537   .0312806     3.00   0.004     .0314116    .1560958
Zeng_all0_invdist_pc_yr1859 |    .072344    .030833     2.35   0.022      .010894     .133794
Zeng_all0_invdist_pc_yr1860 |    .093601   .0299402     3.13   0.003     .0339304    .1532717
Zeng_all0_invdist_pc_yr1861 |   .0887268    .030921     2.87   0.005     .0271012    .1503523
Zeng_all0_invdist_pc_yr1862 |   .1175102   .0310462     3.79   0.000     .0556352    .1793851
Zeng_all0_invdist_pc_yr1863 |   .1073672   .0299785     3.58   0.001     .0476201    .1671143
Zeng_all0_invdist_pc_yr1864 |   .1038242   .0349045     2.97   0.004     .0342595    .1733888
               capital_Post |  -1.409048   .3833758    -3.68   0.000    -2.173115   -.6449813
            lnurbanpop_Post |   .4734599   .1405673     3.37   0.001     .1933096    .7536101
              lnjinshi_Post |   .4051828   .1363287     2.97   0.004     .1334801    .6768855
              lnquotas_Post |  -1.175197   .5334898    -2.20   0.031     -2.23844    -.111953
                route1_Post |  -.0724558   .2692708    -0.27   0.789    -.6091118    .4642002
          dist_nanjing_Post |  -.9831852   .9922057    -0.99   0.325    -2.960648    .9942778
               mainriv_Post |  -.6709632   .2754552    -2.44   0.017    -1.219945   -.1219818
            dist2canal_Post |   .1890491   1.079485     0.18   0.861    -1.962362     2.34046
               lnwheat_Post |   5.687431   2.337597     2.43   0.017     1.028606    10.34626
                lnrice_Post |  -3.045239   1.318348    -2.31   0.024    -5.672703   -.4177746
                 lnpop_Post |   .2015364   .3279308     0.61   0.541    -.4520287    .8551015
                lnarea_Post |   .7573249   .3352931     2.26   0.027     .0890867    1.425563
                      _cons |  -3.918913   4.774256    -0.82   0.414    -13.43399    5.596166
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Placebo_Yearly_ctrl_all0_invdist.doc, keep(Zeng_all0_invdist_pc_yr* )   se  bdec(3) rde
> c(3) nocons append 
Results\Placebo_Yearly_ctrl_all0_invdist.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_invdist_pc, replace)
file Results\Placebo_Yearly_ctrl_all0_invdist_pc.dta saved

. 
. 
. 
. *** *** *** *** *** *** *** *** *** *** *** 
. *** *** *** *** *** *** *** *** *** *** *** per capita unweighted
. 
. 
. xi: reghdfe  lnmartyr1 Zeng_all0_pc_yr*    capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_
> Post  dist_nanjing_Post mainriv_Post dist2canal_Post lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     , ab
> sorb(year cntyid  prefidXyear)   cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  26,     73) =       4.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6720
                                                  Adj R-squared   =     0.5453
                                                  Within R-sq.    =     0.1215
Number of clusters (cntyid)  =         74         Root MSE        =     1.1135

                                       (Std. Err. adjusted for 74 clusters in cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
          lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
Zeng_all0_pc_yr1850 |   .0067944   .0125104     0.54   0.589    -.0181388    .0317276
Zeng_all0_pc_yr1851 |  -.0003576   .0109527    -0.03   0.974    -.0221863    .0214711
Zeng_all0_pc_yr1852 |   .0067134   .0233702     0.29   0.775    -.0398633    .0532901
Zeng_all0_pc_yr1854 |   .0703474   .0236058     2.98   0.004     .0233011    .1173938
Zeng_all0_pc_yr1855 |   .0512186   .0254553     2.01   0.048     .0004862    .1019509
Zeng_all0_pc_yr1856 |   .0329745   .0291646     1.13   0.262    -.0251505    .0910996
Zeng_all0_pc_yr1857 |   .0717524   .0251847     2.85   0.006     .0215593    .1219454
Zeng_all0_pc_yr1858 |   .0685763   .0234164     2.93   0.005     .0219076     .115245
Zeng_all0_pc_yr1859 |   .0628028   .0212004     2.96   0.004     .0205504    .1050553
Zeng_all0_pc_yr1860 |   .0752207   .0271293     2.77   0.007      .021152    .1292893
Zeng_all0_pc_yr1861 |   .0654837   .0264143     2.48   0.015       .01284    .1181273
Zeng_all0_pc_yr1862 |   .0793347   .0259184     3.06   0.003     .0276794    .1309901
Zeng_all0_pc_yr1863 |   .0749637   .0273777     2.74   0.008        .0204    .1295274
Zeng_all0_pc_yr1864 |   .0711389   .0307913     2.31   0.024      .009772    .1325059
       capital_Post |  -1.361217   .3965916    -3.43   0.001    -2.151623   -.5708107
    lnurbanpop_Post |    .438591   .1339814     3.27   0.002     .1715665    .7056156
      lnjinshi_Post |   .3472428   .1458756     2.38   0.020     .0565131    .6379725
      lnquotas_Post |  -1.165929   .5521971    -2.11   0.038    -2.266456   -.0654013
        route1_Post |  -.0483474   .2794566    -0.17   0.863    -.6053036    .5086088
  dist_nanjing_Post |  -1.211584   .9834865    -1.23   0.222    -3.171669    .7485022
       mainriv_Post |  -.6528719   .2727896    -2.39   0.019    -1.196541    -.109203
    dist2canal_Post |   .4237847   1.056128     0.40   0.689    -1.681075    2.528644
       lnwheat_Post |    5.39865   2.294817     2.35   0.021     .8250873    9.972213
        lnrice_Post |  -3.060719   1.265266    -2.42   0.018    -5.582391    -.539048
         lnpop_Post |   .1889792   .3165519     0.60   0.552    -.4419078    .8198662
        lnarea_Post |   .8476787   .3290088     2.58   0.012      .191965    1.503392
              _cons |  -3.801047   4.718312    -0.81   0.423    -13.20463    5.602535
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Placebo_Yearly_ctrl_all0_invdist.doc, keep(Zeng_all0_pc_yr* )   se  bdec(3) rdec(3) noc
> ons append 
Results\Placebo_Yearly_ctrl_all0_invdist.doc
dir : seeout

. parmest, saving( Results\Placebo_Yearly_ctrl_all0_pc, replace)
file Results\Placebo_Yearly_ctrl_all0_pc.dta saved

. 
. 
. 
. 
. ********************************* Graphing
. *********************************
. *********************************
. preserve

. use Results\Placebo_Yearly_ctrl_all0_invdist, clear

. 
. 
. gen i=_n

. keep if i<=15
(12 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. 
. replace time=time+1 if time>3
(12 real changes made)

. replace time=4 if time==16
(1 real change made)

. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)
(1 real change made)

. 
. replace time=1849+time
(15 real changes made)

. 
. 
. ****
. sort time

. label var time "Year"

.  
. 
. twoway (rcap max95 min95 time, ylabel(-0.25(0.25)0.5) lstyle(ci)  xtitle(" ") )   ///
>   (connect estimate time, lp(solid) lc(black) lw(medthick) xline(1853, lpattern(solid) lcolor(blue) lw(medthi
> ck)) xsize(6) ysize(6)) ///
>        (scatter estimate time, mstyle(p1)  yline(0, lpattern(solid) lcolor(red))  ///
>            legend(off) xlabel(1850(5)1865) graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(whit
> e))  saving(Results\Yearly1_All0_invdist, replace) title(A. Weighted connections, size(medsmall)))
(file Results\Yearly1_All0_invdist.gph saved)

.    
.    
. restore

. 
. 
. 
. 
. *********************************
. preserve

. use Results\Placebo_Yearly_ctrl_all0, clear

. 
. 
. gen i=_n

. keep if i<=15
(12 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. 
. replace time=time+1 if time>3
(12 real changes made)

. replace time=4 if time==16
(1 real change made)

. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)
(1 real change made)

. 
. replace time=1849+time
(15 real changes made)

. 
. 
. ****
. sort time

. label var time "Year"

.  
. 
. twoway (rcap max95 min95 time, ylabel(-0.25(0.25)0.5) lstyle(ci)  xtitle(" ") )   ///
>   (connect estimate time, lp(solid) lc(black) lw(medthick) xline(1853, lpattern(solid) lcolor(blue) lw(medthi
> ck)) xsize(6) ysize(6)) ///
>        (scatter estimate time, mstyle(p1)  yline(0, lpattern(solid) lcolor(red))  ///
>             legend(off) xlabel(1850(5)1865) graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(whi
> te))  saving(Results\Yearly1_All0, replace) title(B. Unweighted connections, size(medsmall)))
(file Results\Yearly1_All0.gph saved)

.    
.    
. restore

. 
. 
. 
. 
. *********************************
. preserve

. use Results\Placebo_Yearly_ctrl_all0_invdist_pc, clear

. 
. 
. gen i=_n

. keep if i<=15
(12 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. 
. replace time=time+1 if time>3
(12 real changes made)

. replace time=4 if time==16
(1 real change made)

. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)
(1 real change made)

. 
. replace time=1849+time
(15 real changes made)

. 
. 
. ****
. sort time

. label var time "Year"

.  
.  
. twoway (rcap max95 min95 time, ylabel(-0.08(0.08)0.16) lstyle(ci)  xtitle(" ") )   ///
>   (connect estimate time, lp(solid) lc(black) lw(medthick) xline(1853, lpattern(solid) lcolor(blue) lw(medthi
> ck)) xsize(6) ysize(6)) ///
>        (scatter estimate time, mstyle(p1)  yline(0, lpattern(solid) lcolor(red))  ///
>            legend(order(2 "Effects of elite connections")  size(small) row(1) region(color(none)))  xlabel(18
> 50(5)1865) graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(white))  saving(Results\Yearly1_All0
> _invdist_pc, replace) title(C. Weighted connections per capita, size(medsmall)))
(file Results\Yearly1_All0_invdist_pc.gph saved)

.            
. restore

. 
. 
. 
. 
. *********************************
. preserve

. use Results\Placebo_Yearly_ctrl_all0_pc, clear

. 
. 
. gen i=_n

. keep if i<=15
(12 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. 
. replace time=time+1 if time>3
(12 real changes made)

. replace time=4 if time==16
(1 real change made)

. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==4
  3. }
(1 real change made)
(1 real change made)

. 
. replace time=1849+time
(15 real changes made)

. 
. 
. ****
. sort time

. label var time "Year"

.  
.  
.   
. twoway (rcap max95 min95 time, ylabel(-0.08(0.08)0.16) lstyle(ci)  xtitle(" ") )   ///
>   (connect estimate time, lp(solid) lc(black) lw(medthick)  xline(1853, lpattern(solid) lcolor(blue) lw(medth
> ick)) xsize(6) ysize(6)) ///
>        (scatter estimate time, mstyle(p1)  yline(0, lpattern(solid) lcolor(red))  ///
>            legend(order(1 "95% CI")  size(small) row(1) region(color(none)))  xlabel(1850(5)1865) graphregion
> (color(white) ifcolor(white) ilcolor(white) fcolor(white))  saving(Results\Yearly1_All0_pc, replace) title(D.
>  Unweighted connections per capita, size(medsmall)))
(file Results\Yearly1_All0_pc.gph saved)

.    
.    
. restore

. 
. 
. gr combine Results\Yearly1_All0_invdist.gph  Results\Yearly1_All0.gph Results\Yearly1_All0_invdist_pc.gph  Re
> sults\Yearly1_All0_pc.gph  , row(2)  ysize(18) xsize(15)  graphregion(color(white) ifcolor(white) ilcolor(whi
> te) fcolor(white))

. graph export Results\Figure_4.png, replace
(file Results\Figure_4.png written in PNG format)

. 
. 
end of do-file

. 
. 
. ******* Table B.4. The Impact of Elite Connections on Soldier Deaths: Controlling for Physical Distance to Ze
> ng
. 
. 
. do Programs\Appendix_Table_B4.do

. 
. ************************************************************************************************
. ****Table B4: The Impact of Elite Connections on Soldier Deaths: Controlling for Physical Distance to Zeng
. *** Distance to Xiangxiang  dist_xiangxiang_Post   LangSimilarity_Post 
. ************************************************************************************************
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist invdist0_L1 invdist0_F1 lnarea capital lnurbanp
> op  lnpop  dist_nanjing lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice dist_xiangxiang LangSimila
> rity{
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. 
. ********************************************************************************
. ************************** regression
. 
. reghdfe   lnmartyr1 capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post
>  mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  dist_xiangxiang_Post     Zen
> g_all0_invdist_Post  ,   absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  14,     73) =       5.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6709
                                                  Adj R-squared   =     0.5506
                                                  Within R-sq.    =     0.1186
Number of clusters (cntyid)  =         74         Root MSE        =     1.1070

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.246431   .3890443    -3.20   0.002    -2.021795   -.4710669
       lnurbanpop_Post |    .372432   .1309336     2.84   0.006     .1114817    .6333824
         lnjinshi_Post |   .3150729   .1395346     2.26   0.027     .0369807     .593165
         lnquotas_Post |  -.6118133    .511827    -1.20   0.236    -1.631883    .4082565
           route1_Post |  -.2355956   .2460212    -0.96   0.341    -.7259151    .2547239
     dist_nanjing_Post |  -.4641581   1.175188    -0.39   0.694    -2.806304    1.877988
          mainriv_Post |  -.7953644   .2601238    -3.06   0.003     -1.31379   -.2769385
       dist2canal_Post |  -.2935082   1.190443    -0.25   0.806    -2.666057     2.07904
          lnwheat_Post |   6.755266   2.450133     2.76   0.007     1.872157    11.63837
           lnrice_Post |  -3.717164    1.43105    -2.60   0.011    -6.569243   -.8650858
            lnpop_Post |  -.2851691   .3527342    -0.81   0.421    -.9881673    .4178292
           lnarea_Post |   .8203339   .2968117     2.76   0.007      .228789    1.411879
  dist_xiangxiang_Post |  -.0073688   .0042219    -1.75   0.085     -.015783    .0010455
Zeng_all0_invdist_Post |   .2602411   .0656067     3.97   0.000     .1294871    .3909951
                 _cons |   .1446057   4.427037     0.03   0.974    -8.678467    8.967678
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_invdist_Post) se  bdec(3) rdec(3) nocons replace 
Results\Appendix_Table_B4.doc
dir : seeout

. 
. 
. **************
. reghdfe   lnmartyr1 capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post
>  mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     LangSimilarity_Post Zeng_
> all0_invdist_Post ,  absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  14,     73) =       4.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6693
                                                  Adj R-squared   =     0.5484
                                                  Within R-sq.    =     0.1142
Number of clusters (cntyid)  =         74         Root MSE        =     1.1098

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.538391   .4500679    -3.42   0.001    -2.435375   -.6414071
       lnurbanpop_Post |   .4682463   .1512839     3.10   0.003      .166738    .7697545
         lnjinshi_Post |   .4384239   .1582373     2.77   0.007     .1230573    .7537904
         lnquotas_Post |  -1.039693   .5635977    -1.84   0.069    -2.162941     .083556
           route1_Post |  -.1873315    .249972    -0.75   0.456    -.6855251     .310862
     dist_nanjing_Post |  -1.728399   1.087629    -1.59   0.116    -3.896041    .4392434
          mainriv_Post |  -.7175738   .2660046    -2.70   0.009     -1.24772   -.1874273
       dist2canal_Post |   .9053306   1.127538     0.80   0.425     -1.34185    3.152511
          lnwheat_Post |   5.073345   2.446899     2.07   0.042     .1966821    9.950008
           lnrice_Post |  -4.280621   1.528316    -2.80   0.007     -7.32655   -1.234691
            lnpop_Post |  -.1119267   .3494485    -0.32   0.750    -.8083765     .584523
           lnarea_Post |   .8892772   .3377246     2.63   0.010     .2161932    1.562361
   LangSimilarity_Post |    -.49226   .6004101    -0.82   0.415    -1.688876    .7043557
Zeng_all0_invdist_Post |   .2774477   .0786622     3.53   0.001     .1206741    .4342213
                 _cons |   .3807549   4.896081     0.08   0.938     -9.37712    10.13863
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_invdist_Post ) se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B4.doc
dir : seeout

. 
. 
. **************
. 
. reghdfe   lnmartyr1 capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post
>  mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  dist_xiangxiang_Post   LangS
> imilarity_Post  Zeng_all0_invdist_Post ,  absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  15,     73) =       6.45
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6728
                                                  Adj R-squared   =     0.5526
                                                  Within R-sq.    =     0.1236
Number of clusters (cntyid)  =         74         Root MSE        =     1.1046

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.404632   .4054274    -3.46   0.001    -2.212648   -.5966166
       lnurbanpop_Post |   .4235404   .1377415     3.07   0.003     .1490221    .6980588
         lnjinshi_Post |   .3938981   .1509362     2.61   0.011     .0930827    .6947135
         lnquotas_Post |  -.8216121   .5326847    -1.54   0.127    -1.883251    .2400269
           route1_Post |  -.2563599   .2530585    -1.01   0.314    -.7607048    .2479849
     dist_nanjing_Post |   -.831375   1.154578    -0.72   0.474    -3.132445    1.469695
          mainriv_Post |  -.8445014   .2505835    -3.37   0.001    -1.343914   -.3450892
       dist2canal_Post |   .1399617   1.150382     0.12   0.903    -2.152747     2.43267
          lnwheat_Post |   7.567102   2.620942     2.89   0.005     2.343573    12.79063
           lnrice_Post |  -4.111034    1.42557    -2.88   0.005     -6.95219   -1.269877
            lnpop_Post |   -.421865    .359221    -1.17   0.244    -1.137791    .2940614
           lnarea_Post |   .9084987   .2881454     3.15   0.002     .3342257    1.482772
  dist_xiangxiang_Post |  -.0104246   .0048894    -2.13   0.036    -.0201692     -.00068
   LangSimilarity_Post |  -1.089829   .6229106    -1.75   0.084    -2.331288    .1516296
Zeng_all0_invdist_Post |    .295204   .0681616     4.33   0.000     .1593581    .4310499
                 _cons |   .5859547   4.276728     0.14   0.891    -7.937551    9.109461
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_invdist_Post) se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B4.doc
dir : seeout

. 
. 
. ***************
. reghdfe   lnmartyr1 capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post
>  mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post dist_xiangxiang_Post     Zeng
> _all0_Post ,   absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  14,     73) =       4.99
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6704
                                                  Adj R-squared   =     0.5498
                                                  Within R-sq.    =     0.1171
Number of clusters (cntyid)  =         74         Root MSE        =     1.1080

                                        (Std. Err. adjusted for 74 clusters in cntyid)
--------------------------------------------------------------------------------------
                     |               Robust
           lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        capital_Post |  -1.201879   .3829733    -3.14   0.002    -1.965143   -.4386142
     lnurbanpop_Post |   .3462008   .1302749     2.66   0.010     .0865632    .6058384
       lnjinshi_Post |   .2658754   .1504102     1.77   0.081    -.0338917    .5656425
       lnquotas_Post |  -.6363925   .5200706    -1.22   0.225    -1.672892    .4001066
         route1_Post |  -.2122634   .2564818    -0.83   0.411    -.7234309     .298904
   dist_nanjing_Post |  -.6443605   1.201544    -0.54   0.593    -3.039035    1.750314
        mainriv_Post |  -.7868839   .2628252    -2.99   0.004    -1.310694    -.263074
     dist2canal_Post |  -.1033212   1.193106    -0.09   0.931    -2.481178    2.274536
        lnwheat_Post |    6.65219   2.470654     2.69   0.009     1.728184     11.5762
         lnrice_Post |    -3.5816   1.432284    -2.50   0.015    -6.436137   -.7270631
          lnpop_Post |  -.2478557   .3481272    -0.71   0.479    -.9416721    .4459608
         lnarea_Post |   .8801065   .2953514     2.98   0.004     .2914721    1.468741
dist_xiangxiang_Post |  -.0072853    .004492    -1.62   0.109    -.0162378    .0016672
      Zeng_all0_Post |   .1804037     .05262     3.43   0.001     .0755322    .2852753
               _cons |  -.4182188   4.509072    -0.09   0.926    -9.404787     8.56835
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_Post) se  bdec(3) rdec(3) nocons append  
Results\Appendix_Table_B4.doc
dir : seeout

. 
. 
. **************
. reghdfe   lnmartyr1 capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Post
>  mainriv_Post dist2canal_Post  lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  LangSimilarity_Post   Zeng_al
> l0_Post ,   absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  14,     73) =       4.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6686
                                                  Adj R-squared   =     0.5474
                                                  Within R-sq.    =     0.1124
Number of clusters (cntyid)  =         74         Root MSE        =     1.1109

                                       (Std. Err. adjusted for 74 clusters in cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
          lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
       capital_Post |  -1.458004   .4384338    -3.33   0.001    -2.331801   -.5842066
    lnurbanpop_Post |   .4310673   .1467215     2.94   0.004     .1386518    .7234828
      lnjinshi_Post |   .3738089   .1628661     2.30   0.025     .0492171    .6984006
      lnquotas_Post |  -1.024445   .5702781    -1.80   0.077    -2.161008    .1121173
        route1_Post |  -.1639164   .2531196    -0.65   0.519    -.6683831    .3405503
  dist_nanjing_Post |  -1.819008   1.103557    -1.65   0.104    -4.018393    .3803772
       mainriv_Post |  -.7084013   .2681938    -2.64   0.010    -1.242911   -.1738918
    dist2canal_Post |   1.003554   1.121415     0.89   0.374    -1.231424    3.238532
       lnwheat_Post |   4.971571   2.426065     2.05   0.044     .1364308    9.806711
        lnrice_Post |   -4.07288   1.525849    -2.67   0.009    -7.113892   -1.031868
         lnpop_Post |  -.0688923   .3536216    -0.19   0.846     -.773659    .6358745
        lnarea_Post |    .939253   .3397197     2.76   0.007     .2621926    1.616313
LangSimilarity_Post |   -.362863   .6236603    -0.58   0.562    -1.605816    .8800902
     Zeng_all0_Post |   .1885782    .056958     3.31   0.001     .0750611    .3020954
              _cons |  -.2527922   5.058085    -0.05   0.960    -10.33354    9.827958
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_Post ) se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B4.doc
dir : seeout

. 
. **************
. 
. reghdfe   lnmartyr1  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Post  dist_nanjing_Pos
> t mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post dist_xiangxiang_Post   LangS
> imilarity_Post  Zeng_all0_Post ,  absorb(year cntyid prefidXyear) cluster(  cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  15,     73) =       5.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6718
                                                  Adj R-squared   =     0.5511
                                                  Within R-sq.    =     0.1208
Number of clusters (cntyid)  =         74         Root MSE        =     1.1064

                                        (Std. Err. adjusted for 74 clusters in cntyid)
--------------------------------------------------------------------------------------
                     |               Robust
           lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        capital_Post |  -1.312599    .401637    -3.27   0.002     -2.11306   -.5121377
     lnurbanpop_Post |   .3868361    .138122     2.80   0.007     .1115593    .6621128
       lnjinshi_Post |   .3306648   .1596949     2.07   0.042     .0123934    .6489362
       lnquotas_Post |  -.8194714   .5438666    -1.51   0.136    -1.903396    .2644532
         route1_Post |  -.2298213   .2652056    -0.87   0.389    -.7583752    .2987326
   dist_nanjing_Post |  -.9790341   1.198001    -0.82   0.416    -3.366646    1.408578
        mainriv_Post |  -.8291209   .2584234    -3.21   0.002    -1.344158   -.3140839
     dist2canal_Post |   .2884284   1.169565     0.25   0.806    -2.042511    2.619368
        lnwheat_Post |   7.308816   2.629092     2.78   0.007     2.069043    12.54859
         lnrice_Post |  -3.901691   1.440037    -2.71   0.008    -6.771681     -1.0317
          lnpop_Post |  -.3595405   .3602564    -1.00   0.322     -1.07753    .3584494
         lnarea_Post |   .9567587   .2913242     3.28   0.002     .3761504    1.537367
dist_xiangxiang_Post |  -.0098392    .005105    -1.93   0.058    -.0200135    .0003351
 LangSimilarity_Post |  -.9125415   .6152937    -1.48   0.142     -2.13882    .3137371
      Zeng_all0_Post |   .1967521   .0545248     3.61   0.001     .0880843    .3054198
               _cons |  -.0562955   4.442788    -0.01   0.990    -8.910759    8.798168
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B4.doc, keep(Zeng_all0_Post) se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B4.doc
dir : seeout

. 
.  
.  
. 
end of do-file

. 
. 
. ******* Table B.5. I. Elite Networks and Data Missing
. 
. 
. do Programs\Appendix_Table_B5_I.do

. 
. 
. *********************************************************************************
. ********************************** Table B.5. I. Elite Networks and Data Missing
. *********************************************************************************
. 
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist invdist0_L1 invdist0_F1 lnarea capital lnurbanp
> op  lnpop  dist_nanjing lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice dist_xiangxiang LangSimila
> rity{
  2. gen `y'_Post=`y'*Post
  3. 
. }

. 
.  
. ************************************************************************************************
. ************************ regression 
. 
. reghdfe   missingratio Zeng_all0_invdist_Post  if year==1855,  absorb(prefid)  cluster( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(   1,     58) =       0.40
Statistics robust to heteroskedasticity           Prob > F        =     0.5294
                                                  R-squared       =     0.1910
                                                  Adj R-squared   =    -0.0043
                                                  Within R-sq.    =     0.0022
Number of clusters (cntyid)  =         73         Root MSE        =     0.1517

                                          (Std. Err. adjusted for 73 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
          missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .0030971   .0048953     0.63   0.529    -.0067019    .0128961
                 _cons |   .0491888   .0198523     2.48   0.016     .0094501    .0889276
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_invdist_Post)   se  bdec(3) rdec(3) nocons repl
> ace 
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe   missingratio Zeng_all0_invdist_Post lntotmartyr_withmissing if year==1855,  absorb(prefid)  cluster
> ( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(   2,     57) =       2.31
Statistics robust to heteroskedasticity           Prob > F        =     0.1085
                                                  R-squared       =     0.2456
                                                  Adj R-squared   =     0.0470
                                                  Within R-sq.    =     0.0695
Number of clusters (cntyid)  =         73         Root MSE        =     0.1478

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
           missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
 Zeng_all0_invdist_Post |  -.0018766   .0048088    -0.39   0.698     -.011506    .0077529
lntotmartyr_withmissing |   .0287835   .0138283     2.08   0.042     .0010927    .0564742
                  _cons |  -.0804399    .058241    -1.38   0.173    -.1970655    .0361858
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_invdist_Post lntotmartyr_withmissing)   se  bde
> c(3) rdec(3) nocons append  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe  missingratio  Zeng_all0_invdist_Post  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post rou
> te1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post   
>    if year==1855,  absorb(prefid)  cluster(cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(  13,     46) =       0.68
Statistics robust to heteroskedasticity           Prob > F        =     0.7735
                                                  R-squared       =     0.2723
                                                  Adj R-squared   =    -0.1390
                                                  Within R-sq.    =     0.1025
Number of clusters (cntyid)  =         73         Root MSE        =     0.1615

                                          (Std. Err. adjusted for 73 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
          missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .0136881   .0134599     1.02   0.314    -.0134052    .0407815
          capital_Post |  -.0810217   .0968002    -0.84   0.407    -.2758704     .113827
       lnurbanpop_Post |   .0255243   .0198532     1.29   0.205    -.0144381    .0654867
         lnjinshi_Post |  -.0228962   .0465853    -0.49   0.625    -.1166676    .0708752
         lnquotas_Post |   .0878078    .077817     1.13   0.265    -.0688296    .2444453
           route1_Post |   .0266329   .0827659     0.32   0.749    -.1399663    .1932321
     dist_nanjing_Post |   .0889735   .1806245     0.49   0.625    -.2746048    .4525518
          mainriv_Post |  -.0148478   .0874597    -0.17   0.866    -.1908951    .1611995
       dist2canal_Post |  -.1443197   .1740021    -0.83   0.411    -.4945677    .2059283
          lnwheat_Post |  -.4571881   .4227957    -1.08   0.285    -1.308232    .3938556
           lnrice_Post |   .2382633   .2474025     0.96   0.341    -.2597321    .7362586
            lnpop_Post |  -.0538514   .0661854    -0.81   0.420    -.1870756    .0793728
           lnarea_Post |   .0570375   .0521922     1.09   0.280      -.04802     .162095
                 _cons |   .6806745   1.006137     0.68   0.502    -1.344575    2.705924
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_invdist_Post)   se  bdec(3) rdec(3) nocons appe
> nd  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe   missingratio  Zeng_all0_invdist_Post lntotmartyr_withmissing  capital_Post lnurbanpop_Post  lnjinsh
> i_Post  lnquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post 
> lnpop_Post lnarea_Post     if year==1855,  absorb(prefid)  cluster( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(  14,     45) =       0.86
Statistics robust to heteroskedasticity           Prob > F        =     0.6009
                                                  R-squared       =     0.2982
                                                  Adj R-squared   =    -0.1229
                                                  Within R-sq.    =     0.1344
Number of clusters (cntyid)  =         73         Root MSE        =     0.1604

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
           missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
 Zeng_all0_invdist_Post |   .0103154   .0131326     0.79   0.436    -.0161351    .0367659
lntotmartyr_withmissing |   .0223481   .0182417     1.23   0.227    -.0143925    .0590887
           capital_Post |   -.068284    .097821    -0.70   0.489    -.2653057    .1287377
        lnurbanpop_Post |   .0155124   .0222259     0.70   0.489    -.0292528    .0602775
          lnjinshi_Post |  -.0232929    .046525    -0.50   0.619    -.1169991    .0704133
          lnquotas_Post |   .0814132   .0797812     1.02   0.313    -.0792744    .2421008
            route1_Post |   .0366026   .0835152     0.44   0.663    -.1316057    .2048109
      dist_nanjing_Post |   .0783377   .1805924     0.43   0.667     -.285394    .4420694
           mainriv_Post |  -.0015392   .0908132    -0.02   0.987    -.1844463    .1813679
        dist2canal_Post |  -.1210104   .1707778    -0.71   0.482    -.4649746    .2229538
           lnwheat_Post |  -.3838587   .3729248    -1.03   0.309    -1.134968    .3672504
            lnrice_Post |   .2183245   .2300093     0.95   0.348     -.244938    .6815869
             lnpop_Post |  -.0521459   .0625624    -0.83   0.409     -.178153    .0738611
            lnarea_Post |   .0422461   .0503383     0.84   0.406    -.0591404    .1436326
                  _cons |   .5708437    .977512     0.58   0.562    -1.397967    2.539654
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_invdist_Post lntotmartyr_withmissing )   se  bd
> ec(3) rdec(3) nocons append  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. ********************
. reghdfe   missingratio Zeng_all0_Post  if year==1855,  absorb(prefid)  cluster( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(   1,     58) =       0.21
Statistics robust to heteroskedasticity           Prob > F        =     0.6468
                                                  R-squared       =     0.1903
                                                  Adj R-squared   =    -0.0052
                                                  Within R-sq.    =     0.0013
Number of clusters (cntyid)  =         73         Root MSE        =     0.1518

                                  (Std. Err. adjusted for 73 clusters in cntyid)
--------------------------------------------------------------------------------
               |               Robust
  missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
Zeng_all0_Post |   .0016819   .0036512     0.46   0.647    -.0056268    .0089906
         _cons |   .0500898   .0204331     2.45   0.017     .0091885    .0909911
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_Post)   se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe   missingratio Zeng_all0_Post lntotmartyr_withmissing if year==1855,  absorb(prefid)  cluster( cntyid
> )
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(   2,     57) =       2.28
Statistics robust to heteroskedasticity           Prob > F        =     0.1119
                                                  R-squared       =     0.2460
                                                  Adj R-squared   =     0.0475
                                                  Within R-sq.    =     0.0700
Number of clusters (cntyid)  =         73         Root MSE        =     0.1477

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
           missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
         Zeng_all0_Post |  -.0016716   .0033758    -0.50   0.622    -.0084316    .0050884
lntotmartyr_withmissing |   .0289894   .0138384     2.09   0.041     .0012784    .0567004
                  _cons |  -.0807962   .0582399    -1.39   0.171    -.1974196    .0358273
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_Post lntotmartyr_withmissing)   se  bdec(3) rde
> c(3) nocons append  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe   missingratio  Zeng_all0_Post  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_Pos
> t  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post    if yea
> r==1855,  absorb(prefid)  cluster( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(  13,     46) =       0.63
Statistics robust to heteroskedasticity           Prob > F        =     0.8126
                                                  R-squared       =     0.2699
                                                  Adj R-squared   =    -0.1428
                                                  Within R-sq.    =     0.0995
Number of clusters (cntyid)  =         73         Root MSE        =     0.1618

                                     (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
     missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
   Zeng_all0_Post |   .0089239   .0087976     1.01   0.316    -.0087848    .0266326
     capital_Post |  -.0760204    .092155    -0.82   0.414    -.2615188     .109478
  lnurbanpop_Post |    .024313   .0196739     1.24   0.223    -.0152885    .0639145
    lnjinshi_Post |  -.0253383    .047771    -0.53   0.598    -.1214963    .0708197
    lnquotas_Post |   .0862075   .0772208     1.12   0.270    -.0692298    .2416449
      route1_Post |   .0273677   .0828029     0.33   0.743    -.1393058    .1940412
dist_nanjing_Post |   .0787274   .1739322     0.45   0.653    -.2713799    .4288347
     mainriv_Post |  -.0151442   .0872964    -0.17   0.863    -.1908627    .1605744
  dist2canal_Post |  -.1335584   .1666203    -0.80   0.427    -.4689478    .2018309
     lnwheat_Post |  -.4645782   .4237713    -1.10   0.279    -1.317586    .3884292
      lnrice_Post |   .2467566   .2496537     0.99   0.328    -.2557704    .7492835
       lnpop_Post |  -.0521645   .0665354    -0.78   0.437    -.1860934    .0817643
      lnarea_Post |   .0598379   .0530331     1.13   0.265    -.0469123     .166588
            _cons |    .647963    1.00509     0.64   0.522    -1.375178    2.671104
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_Post lntotmartyr_withmissing)   se  bdec(3) rde
> c(3) nocons append  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
. reghdfe  missingratio  Zeng_all0_Post lntotmartyr_withmissing  capital_Post lnurbanpop_Post  lnjinshi_Post  l
> nquotas_Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Pos
> t lnarea_Post   if year==1855,  absorb(prefid)  cluster( cntyid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         73
Absorbing 1 HDFE group                            F(  14,     45) =       0.83
Statistics robust to heteroskedasticity           Prob > F        =     0.6371
                                                  R-squared       =     0.2969
                                                  Adj R-squared   =    -0.1250
                                                  Within R-sq.    =     0.1328
Number of clusters (cntyid)  =         73         Root MSE        =     0.1606

                                           (Std. Err. adjusted for 73 clusters in cntyid)
-----------------------------------------------------------------------------------------
                        |               Robust
           missingratio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
         Zeng_all0_Post |   .0067231   .0083837     0.80   0.427    -.0101626    .0236087
lntotmartyr_withmissing |    .022757   .0182272     1.25   0.218    -.0139544    .0594685
           capital_Post |   -.064462   .0924674    -0.70   0.489     -.250701     .121777
        lnurbanpop_Post |   .0144154   .0219359     0.66   0.514    -.0297656    .0585965
          lnjinshi_Post |  -.0251806    .047562    -0.53   0.599    -.1209753    .0706142
          lnquotas_Post |   .0801483   .0792153     1.01   0.317    -.0793995    .2396962
            route1_Post |   .0373609   .0835989     0.45   0.657    -.1310159    .2057377
      dist_nanjing_Post |   .0705354    .173868     0.41   0.687    -.2796528    .4207235
           mainriv_Post |  -.0015065   .0909043    -0.02   0.987    -.1845972    .1815842
        dist2canal_Post |  -.1125967   .1633605    -0.69   0.494    -.4416217    .2164282
           lnwheat_Post |  -.3879544   .3730879    -1.04   0.304    -1.139392    .3634832
            lnrice_Post |   .2244175    .232389     0.97   0.339    -.2436379    .6924729
             lnpop_Post |  -.0508379   .0630576    -0.81   0.424    -.1778425    .0761667
            lnarea_Post |   .0441152   .0511092     0.86   0.393     -.058824    .1470544
                  _cons |   .5435296   .9815693     0.55   0.583    -1.433452    2.520512
-----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B5_I.doc, keep(Zeng_all0_Post lntotmartyr_withmissing)   se  bdec(3) rde
> c(3) nocons append  
Results\Appendix_Table_B5_I.doc
dir : seeout

. 
. 
.  
. 
end of do-file

. 
. 
. ******* Table B.5. II. The Impact of Elite Connections on Soldier Deaths: Degree-Holders vs. Commoners
. 
. 
. do Programs\Appendix_Table_B5_II.do

. 
.  
. ************************************************************************************
. ****** Table B.5. II. The Impact of Elite Connections on Soldier Deaths: Degree-Holders vs. Commoners
. ************************************************************************************
. 
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist invdist0_L1 invdist0_F1 lnarea capital lnurbanp
> op  lnpop  dist_nanjing lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  dist_xiangxiang LangSimil
> arity{
  2. gen `y'_Post=`y'*Post
  3. 
. }

.  
. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. ************************************************************************************************
. *************************** regression
. 
. reghdfe  std_lnmartyr1  Zeng_all0_invdist_Post  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post ro
> ute1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post  
>     ,  absorb(year cntyid prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       4.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6689
                                                  Adj R-squared   =     0.5483
                                                  Within R-sq.    =     0.1130
Number of clusters (cntyid)  =         74         Root MSE        =     0.6737

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
         std_lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .1586984   .0459263     3.46   0.001     .0671673    .2502294
          capital_Post |  -.8693621   .2576107    -3.37   0.001    -1.382779   -.3559448
       lnurbanpop_Post |   .2632631   .0826314     3.19   0.002      .098579    .4279471
         lnjinshi_Post |    .236122   .0865263     2.73   0.008     .0636754    .4085686
         lnquotas_Post |  -.5417027   .3240267    -1.67   0.099    -1.187487    .1040817
           route1_Post |  -.1135437   .1502059    -0.76   0.452    -.4129037    .1858163
     dist_nanjing_Post |  -.8433214   .5951481    -1.42   0.161     -2.02945    .3428069
          mainriv_Post |  -.4316945   .1643357    -2.63   0.010    -.7592151   -.1041739
       dist2canal_Post |   .3346544   .6397563     0.52   0.602    -.9403782    1.609687
          lnwheat_Post |    3.05329   1.479986     2.06   0.043     .1036826    6.002898
           lnrice_Post |  -2.453566   .8771509    -2.80   0.007    -4.201725   -.7054065
            lnpop_Post |  -.0529671   .2070649    -0.26   0.799    -.4656468    .3597126
           lnarea_Post |   .5128592   .1994083     2.57   0.012      .115439    .9102795
                 _cons |   -.584635   2.914872    -0.20   0.842    -6.393967    5.224697
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B5_II.doc, keep( Zeng_all0_invdist_Post )   se  bdec(3) rdec(3) nocons r
> eplace  
Results\Appendix_Table_B5_II.doc
dir : seeout

. 
. reghdfe   std_lnmartyr1  Zeng_all0_Post   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post route1_P
> ost  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post     ,  
> absorb(year cntyid  prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       4.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6684
                                                  Adj R-squared   =     0.5476
                                                  Within R-sq.    =     0.1117
Number of clusters (cntyid)  =         74         Root MSE        =     0.6742

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
    std_lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
   Zeng_all0_Post |    .110403   .0339032     3.26   0.002     .0428339     .177972
     capital_Post |  -.8425811   .2536191    -3.32   0.001    -1.348043   -.3371191
  lnurbanpop_Post |   .2467614   .0801435     3.08   0.003     .0870356    .4064873
    lnjinshi_Post |   .2051735   .0908943     2.26   0.027     .0240214    .3863256
    lnquotas_Post |  -.5542694   .3308512    -1.68   0.098    -1.213655    .1051162
      route1_Post |  -.0993737   .1525365    -0.65   0.517    -.4033785     .204631
dist_nanjing_Post |  -.9460644   .5985436    -1.58   0.118     -2.13896    .2468314
     mainriv_Post |  -.4269045   .1645341    -2.59   0.011    -.7548204   -.0989886
  dist2canal_Post |   .4439973   .6335744     0.70   0.486    -.8187147    1.706709
     lnwheat_Post |   3.003619     1.4684     2.05   0.044     .0771021    5.930135
      lnrice_Post |  -2.368082   .8739178    -2.71   0.008    -4.109797    -.626366
       lnpop_Post |  -.0314502   .2073218    -0.15   0.880    -.4446419    .3817415
      lnarea_Post |   .5495275   .1992989     2.76   0.007     .1523254    .9467297
            _cons |  -.9336418   2.990023    -0.31   0.756    -6.892749    5.025466
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B5_II.doc, keep( Zeng_all0_Post )   se  bdec(3) rdec(3) nocons append  
Results\Appendix_Table_B5_II.doc
dir : seeout

. 
.  
. *****
. reghdfe   std_lnGentryDeath1   Zeng_all0_invdist_Post   capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas
> _Post route1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnare
> a_Post    ,  absorb(year cntyid  prefidXyear)  cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       2.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0172
                                                  R-squared       =     0.4100
                                                  Adj R-squared   =     0.1952
                                                  Within R-sq.    =     0.0398
Number of clusters (cntyid)  =         74         Root MSE        =     0.9028

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
    std_lnGentryDeath1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
Zeng_all0_invdist_Post |   .1480564   .0649012     2.28   0.025     .0187084    .2774043
          capital_Post |  -.7581113   .2711442    -2.80   0.007    -1.298501   -.2177216
       lnurbanpop_Post |  -.0072339   .0839712    -0.09   0.932    -.1745883    .1601206
         lnjinshi_Post |   .1909718   .1211428     1.58   0.119    -.0504654     .432409
         lnquotas_Post |  -.1896921   .3519893    -0.54   0.592    -.8912057    .5118216
           route1_Post |  -.5303245   .2668471    -1.99   0.051     -1.06215     .001501
     dist_nanjing_Post |   1.604761   .6175168     2.60   0.011     .3740514     2.83547
          mainriv_Post |   .1656039    .173559     0.95   0.343    -.1802986    .5115064
       dist2canal_Post |  -1.382745   .6011727    -2.30   0.024     -2.58088   -.1846095
          lnwheat_Post |   1.037854   1.546255     0.67   0.504    -2.043829    4.119536
           lnrice_Post |   .4282356    .899505     0.48   0.635    -1.364475    2.220947
            lnpop_Post |   .2150172   .2158415     1.00   0.322    -.2151543    .6451887
           lnarea_Post |   .0701266   .1637626     0.43   0.670    -.2562517    .3965049
                 _cons |  -5.096302   2.731477    -1.87   0.066    -10.54013    .3475245
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B5_II.doc, keep( Zeng_all0_invdist_Post )   se  bdec(3) rdec(3) nocons a
> ppend 
Results\Appendix_Table_B5_II.doc
dir : seeout

. 
. reghdfe  std_lnGentryDeath1   Zeng_all0_Post  capital_Post lnurbanpop_Post  lnjinshi_Post  lnquotas_Post rout
> e1_Post  dist_nanjing_Post mainriv_Post dist2canal_Post   lnwheat_Post lnrice_Post lnpop_Post lnarea_Post    
>   ,  absorb(year cntyid   prefidXyear)  cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  13,     73) =       2.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0197
                                                  R-squared       =     0.4091
                                                  Adj R-squared   =     0.1939
                                                  Within R-sq.    =     0.0383
Number of clusters (cntyid)  =         74         Root MSE        =     0.9035

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
std_lnGentryDea~1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
   Zeng_all0_Post |   .0988512   .0510654     1.94   0.057     -.002922    .2006245
     capital_Post |  -.7156516   .2704353    -2.65   0.010    -1.254628   -.1766748
  lnurbanpop_Post |  -.0217569   .0884829    -0.25   0.806     -.198103    .1545893
    lnjinshi_Post |   .1666901   .1251179     1.33   0.187    -.0826695    .4160497
    lnquotas_Post |  -.2064808   .3564417    -0.58   0.564    -.9168682    .5039065
      route1_Post |  -.5200132   .2769913    -1.88   0.064    -1.072056    .0320297
dist_nanjing_Post |    1.50103   .6320941     2.37   0.020     .2412678    2.760791
     mainriv_Post |   .1679746   .1752462     0.96   0.341    -.1812906    .5172399
  dist2canal_Post |  -1.272406   .6151282    -2.07   0.042    -2.498355   -.0464576
     lnwheat_Post |    .978252   1.549133     0.63   0.530    -2.109165    4.065669
      lnrice_Post |   .5019477   .8973901     0.56   0.578    -1.286548    2.290444
       lnpop_Post |   .2337228   .2139539     1.09   0.278    -.1926867    .6601322
      lnarea_Post |   .1002572   .1701326     0.59   0.557    -.2388166    .4393311
            _cons |  -5.363337   2.678289    -2.00   0.049    -10.70116   -.0255146
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B5_II.doc, keep( Zeng_all0_Post )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_B5_II.doc
dir : seeout

. 
.  
.  
. 
end of do-file

. 
. 
. ******* Table B.6. I. What Does the Number of Soldier Deaths Measure?
. 
. 
. do Programs\Appendix_Table_B6_I.do

. 
. *This dofile produces Table B6.I column (1)-(3)
. 
. ************************************************************************************
. ****** Table B.6. I. What Does the Number of Soldier Deaths Measure?
. ************************************************************************************
. 
. 
. *This part produces Table B6.I column (1)-(3)
. 
. 
. use Data\HunanCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(825 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(825 real changes made)

. 
. 
. foreach y of varlist Zeng_all0_invdist_pc Zeng_all0_pc Zenghu_all Zenghu_all_invdist  Zeng_all0 Zeng_all0_inv
> dist  Zeng_exam0_invdist  Zeng_BMF_invdist Zeng_Juren_invdist invdist0_L1 invdist0_F1 lnarea capital lnurbanp
> op  lnpop lnhh  dist_nanjing lnjinshi lnquotas mainriv route1 dist2canal lnwheat lnrice  dist_xiangxiang Lang
> Similarity{
  2. gen `y'_Post=`y'*Post
  3. 
. }

.  
.  
. ********************************************************************************
. ********************** gen interactions
. 
. 
. sum lnhh 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        lnhh |      1,125    10.53336    .6204288   8.880446   11.75379

. gen ZengXlnhhXPost=Zeng_all0_invdist_Post*(lnhh-r(mean))

. gen ZenghuXlnhhXPost=Zenghu_all_invdist_Post*(lnhh-r(mean))

. 
. 
. sum lnquotas

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    lnquotas |      1,125    2.625897    .3578146   1.791759   3.135494

. gen ZengXlnquotasXPost=Zeng_all0_invdist_Post*(lnquotas_Post-r(mean))

. 
. 
. sum lnjinshi

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    lnjinshi |      1,125    1.113293    1.068969          0     3.7612

. gen ZengXlnjinshiXPost=Zeng_all0_invdist_Post*(lnjinshi-r(mean))

. 
.  
. 
. **************************
. 
. egen  prefidXyear=group(prefid year)

. 
. 
. ************************************************************************************************
. *************************** regression 
.  
.  
. reghdfe   lnmartyr1  capital_Post lnurbanpop_Post lnjinshi_Post  lnquotas_Post  route1_Post   dist_nanjing_Po
> st mainriv_Post dist2canal_Post lnrice_Post lnwheat_Post   lnhh_Post lnarea_Post Zeng_all0_invdist_Post  Zeng
> XlnquotasXPost ZengXlnhhXPost, absorb(year cntyid prefidXyear) cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  15,     73) =      11.55
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6735
                                                  Adj R-squared   =     0.5535
                                                  Within R-sq.    =     0.1255
Number of clusters (cntyid)  =         74         Root MSE        =     1.1034

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.486755   .3682056    -4.04   0.000    -2.220588   -.7529224
       lnurbanpop_Post |   .4469838   .1392711     3.21   0.002     .1694169    .7245508
         lnjinshi_Post |    .334337   .1499328     2.23   0.029     .0355213    .6331527
         lnquotas_Post |  -.1516007   .5067924    -0.30   0.766    -1.161636     .858435
           route1_Post |   -.143451   .2228618    -0.64   0.522     -.587614     .300712
     dist_nanjing_Post |  -.6928811   .9226934    -0.75   0.455    -2.531806    1.146044
          mainriv_Post |  -.6435089   .2418462    -2.66   0.010    -1.125508   -.1615102
       dist2canal_Post |  -.3304864   1.033792    -0.32   0.750    -2.390832    1.729859
           lnrice_Post |   -3.96222   1.298804    -3.05   0.003    -6.550732   -1.373709
          lnwheat_Post |   6.319306    2.26008     2.80   0.007     1.814974    10.82364
             lnhh_Post |  -.1497628   .2822285    -0.53   0.597    -.7122434    .4127178
           lnarea_Post |   .5282665   .3195149     1.65   0.103    -.1085257    1.165059
Zeng_all0_invdist_Post |   .5332245   .1294192     4.12   0.000     .2752924    .7911566
    ZengXlnquotasXPost |  -.9035565   .2542985    -3.55   0.001    -1.410373   -.3967403
        ZengXlnhhXPost |   .0673776   .0717081     0.94   0.351    -.0755364    .2102916
                 _cons |   .3940412   3.506225     0.11   0.911    -6.593856    7.381938
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B6_I.doc, keep(Zeng_all0_invdist_Post ZengXlnquotasXPost  ZengXlnhhXPost
> )    se  bdec(3) rdec(3) nocons replace 
Results\Appendix_Table_B6_I.doc
dir : seeout

. 
. 
. ***************
. 
. reghdfe   lnmartyr1  capital_Post lnurbanpop_Post lnjinshi_Post  lnquotas_Post  route1_Post   dist_nanjing_Po
> st mainriv_Post dist2canal_Post lnrice_Post lnwheat_Post   lnhh_Post lnarea_Post Zeng_all0_invdist_Post   Zen
> gXlnjinshiXPost ZengXlnhhXPost, absorb(year cntyid prefidXyear) cluster(cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  15,     73) =       6.06
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6712
                                                  Adj R-squared   =     0.5503
                                                  Within R-sq.    =     0.1192
Number of clusters (cntyid)  =         74         Root MSE        =     1.1074

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.527932    .406688    -3.76   0.000     -2.33846   -.7174041
       lnurbanpop_Post |   .4119037   .1382725     2.98   0.004     .1363271    .6874804
         lnjinshi_Post |   .4763566   .1583755     3.01   0.004     .1607146    .7919985
         lnquotas_Post |  -.7369681   .5591345    -1.32   0.192    -1.851322    .3773853
           route1_Post |  -.1195201     .25671    -0.47   0.643    -.6311424    .3921022
     dist_nanjing_Post |  -1.076837   .9583199    -1.12   0.265    -2.986765    .8330922
          mainriv_Post |  -.6500974   .2610445    -2.49   0.015    -1.170358   -.1298365
       dist2canal_Post |   .2493622   1.058695     0.24   0.814    -1.860614    2.359339
           lnrice_Post |  -4.450592    1.37441    -3.24   0.002    -7.189788   -1.711397
          lnwheat_Post |   6.154796   2.427112     2.54   0.013     1.317568    10.99202
             lnhh_Post |  -.2059494   .3124798    -0.66   0.512    -.8287208    .4168219
           lnarea_Post |   .6455794   .3075754     2.10   0.039     .0325826    1.258576
Zeng_all0_invdist_Post |   .5241663   .1628743     3.22   0.002     .1995582    .8487744
    ZengXlnjinshiXPost |  -.1529323   .0795972    -1.92   0.059    -.3115692    .0057047
        ZengXlnhhXPost |   .1124948   .1286831     0.87   0.385    -.1439703    .3689598
                 _cons |   .8868314   3.788043     0.23   0.816    -6.662728    8.436391
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B6_I.doc, keep(Zeng_all0_invdist_Post ZengXlnquotasXPost ZengXlnjinshiXP
> ost ZengXlnhhXPost)    se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_B6_I.doc
dir : seeout

. 
. 
. ***************
. 
. reghdfe   lnmartyr1  capital_Post lnurbanpop_Post lnjinshi_Post  lnquotas_Post  route1_Post   dist_nanjing_Po
> st mainriv_Post dist2canal_Post lnrice_Post lnwheat_Post   lnhh_Post lnarea_Post Zeng_all0_invdist_Post  Zeng
> XlnquotasXPost ZengXlnjinshiXPost ZengXlnhhXPost, absorb(year cntyid prefidXyear) cluster( cntyid)
(dropped 15 singleton observations)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 3 HDFE groups                           F(  16,     73) =      11.91
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6736
                                                  Adj R-squared   =     0.5531
                                                  Within R-sq.    =     0.1257
Number of clusters (cntyid)  =         74         Root MSE        =     1.1039

                                          (Std. Err. adjusted for 74 clusters in cntyid)
----------------------------------------------------------------------------------------
                       |               Robust
             lnmartyr1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
          capital_Post |  -1.499314   .3734175    -4.02   0.000    -2.243534   -.7550939
       lnurbanpop_Post |   .4400644   .1409975     3.12   0.003     .1590568     .721072
         lnjinshi_Post |   .3610478   .1682079     2.15   0.035     .0258099    .6962857
         lnquotas_Post |  -.1765479   .5040825    -0.35   0.727    -1.181183    .8280871
           route1_Post |  -.1396751   .2262148    -0.62   0.539    -.5905206    .3111705
     dist_nanjing_Post |  -.6996328   .9241754    -0.76   0.451    -2.541512    1.142246
          mainriv_Post |  -.6380629   .2441049    -2.61   0.011    -1.124563   -.1515625
       dist2canal_Post |  -.3039475   1.039159    -0.29   0.771    -2.374987    1.767093
           lnrice_Post |  -4.092757   1.353811    -3.02   0.003    -6.790899   -1.394615
          lnwheat_Post |    6.45294   2.337331     2.76   0.007     1.794647    11.11123
             lnhh_Post |  -.1809264   .2977693    -0.61   0.545    -.7743798     .412527
           lnarea_Post |   .5109165   .3127121     1.63   0.107    -.1123179    1.134151
Zeng_all0_invdist_Post |   .5602449   .1445863     3.87   0.000     .2720847     .848405
    ZengXlnquotasXPost |   -.818657   .3247612    -2.52   0.014    -1.465905   -.1714088
    ZengXlnjinshiXPost |   -.037222   .0762664    -0.49   0.627    -.1892208    .1147768
        ZengXlnhhXPost |   .1014971   .0912339     1.11   0.270    -.0803318    .2833261
                 _cons |   .6613378   3.455594     0.19   0.849    -6.225651    7.548327
----------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        15           0          15     |
      cntyid |        74          74           0    *|
 prefidXyear |       210          15         195     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_B6_I.doc, keep(Zeng_all0_invdist_Post ZengXlnquotasXPost ZengXlnjinshiXP
> ost ZengXlnhhXPost)    se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_B6_I.doc
dir : seeout

.   
.  
. 
.  
.  
.  
. ********************************************************************************
. ********************************************************************************
. ********************************************************************************
. ************************************ This dofile creats Table B.6.I Column (4-5)
. 
. 
. 
. use Data\HunanBattleField.dta,clear

. 
. 
. 
. ********************** ********************** ********************** **********
. ********************** gen Zeng Guofan period dummies
. 
. gen Post=0 if year<1854
(47,250 missing values generated)

. replace Post=1 if year>=1854&year<=1864
(47,250 real changes made)

. 
. 
. ******
. 
. gen Zeng_all0_invdist_P=Zeng_all0_invdist*Post

. 
. foreach y of varlist  lnarea capital lnurbanpop lnhh  dist_nanjing  lnjinshi lnquotas  mainriv route1  dist2c
> anal lnrice lnwheat {
  2. gen `y'_Post=`y'*Post
  3. }

. 
. 
. ******
. 
. gen cntyXyear=cntyid*100+(year-1850)

. 
. egen  prefidXyear=group(prefid year)

. 
. 
. 
. ******** Table B.6 Column (4): Without Battle FE
. 
. reghdfe lnmartyrs_battle1 Zeng_all0_invdist_P ///
> capital_Post lnurbanpop_Post lnjinshi_Post  lnquotas_Post  route1_Post   dist_nanjing_Post mainriv_Post dist2
> canal_Post lnrice_Post lnwheat_Post  lnhh_Post lnarea_Post ///
> , absorb(cntyid year prefidXyear) cluster(cntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =     52,050
Absorbing 3 HDFE groups                           F(  13,     74) =       1.19
Statistics robust to heteroskedasticity           Prob > F        =     0.3024
                                                  R-squared       =     0.1959
                                                  Adj R-squared   =     0.1879
                                                  Within R-sq.    =     0.0051
Number of clusters (cntyid)  =         75         Root MSE        =     0.3251

                                       (Std. Err. adjusted for 75 clusters in cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
  lnmartyrs_battle1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
Zeng_all0_invdist_P |   .0336995   .0135456     2.49   0.015     .0067093    .0606897
       capital_Post |  -.1293161    .061435    -2.10   0.039     -.251728   -.0069042
    lnurbanpop_Post |   .0050489   .0117221     0.43   0.668    -.0183078    .0284057
      lnjinshi_Post |   .0103172   .0127991     0.81   0.423    -.0151855    .0358198
      lnquotas_Post |  -.0574066   .0598008    -0.96   0.340    -.1765623    .0617491
        route1_Post |  -.0309076   .0295772    -1.04   0.299    -.0898414    .0280263
  dist_nanjing_Post |  -.0243575   .0371883    -0.65   0.515    -.0984568    .0497418
       mainriv_Post |  -.0460786   .0330066    -1.40   0.167    -.1118456    .0196885
    dist2canal_Post |   .0360544   .0406025     0.89   0.377    -.0448479    .1169566
        lnrice_Post |  -.1291356   .0624492    -2.07   0.042    -.2535684   -.0047028
       lnwheat_Post |   .2545511   .1325225     1.92   0.059    -.0095059     .518608
          lnhh_Post |   .0433873   .0352195     1.23   0.222     -.026789    .1135637
        lnarea_Post |    .031373   .0253859     1.24   0.220    -.0192096    .0819556
              _cons |  -.7369556   .5329145    -1.38   0.171    -1.798811    .3248998
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      cntyid |        75          75           0    *|
        year |        15           0          15     |
 prefidXyear |       430          15         415     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B6_I.doc, keep(Zeng_all0_invdist_P)  se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B6_I.doc
dir : seeout

.  
. 
. ********* Table B.6 Column (5): With Battle FE
. 
. reghdfe lnmartyrs_battle1 Zeng_all0_invdist_P ///
> capital_Post lnurbanpop_Post lnjinshi_Post  lnquotas_Post  route1_Post  dist_nanjing_Post mainriv_Post  dist2
> canal_Post lnrice_Post lnwheat_Post   lnhh_Post lnarea_Post ///
> , absorb(battleid  cntyid year prefidXyear) cluster(cntyid )
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =     52,050
Absorbing 4 HDFE groups                           F(  13,     74) =       1.18
Statistics robust to heteroskedasticity           Prob > F        =     0.3137
                                                  R-squared       =     0.2058
                                                  Adj R-squared   =     0.1871
                                                  Within R-sq.    =     0.0052
Number of clusters (cntyid)  =         75         Root MSE        =     0.3252

                                       (Std. Err. adjusted for 75 clusters in cntyid)
-------------------------------------------------------------------------------------
                    |               Robust
  lnmartyrs_battle1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
Zeng_all0_invdist_P |   .0336995   .0136356     2.47   0.016     .0065299     .060869
       capital_Post |  -.1293161   .0618432    -2.09   0.040    -.2525413   -.0060908
    lnurbanpop_Post |   .0050489   .0117999     0.43   0.670    -.0184629    .0285608
      lnjinshi_Post |   .0103172   .0128841     0.80   0.426    -.0153549    .0359893
      lnquotas_Post |  -.0574066   .0601981    -0.95   0.343    -.1773539    .0625408
        route1_Post |  -.0309076   .0297737    -1.04   0.303     -.090233    .0284178
  dist_nanjing_Post |  -.0243575   .0374354    -0.65   0.517    -.0989491    .0502342
       mainriv_Post |  -.0460786   .0332259    -1.39   0.170    -.1122826    .0201255
    dist2canal_Post |   .0360544   .0408723     0.88   0.381    -.0453854    .1174942
        lnrice_Post |  -.1291356   .0628641    -2.05   0.043    -.2543952   -.0038761
       lnwheat_Post |   .2545511    .133403     1.91   0.060    -.0112603    .5203624
          lnhh_Post |   .0433873   .0354535     1.22   0.225    -.0272553      .11403
        lnarea_Post |    .031373   .0255546     1.23   0.223    -.0195457    .0822917
              _cons |  -.7369556   .5364553    -1.37   0.174    -1.805866     .331955
-------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    battleid |       694           0         694     |
      cntyid |        75          75           0    *|
        year |        15          15           0     |
 prefidXyear |       430          15         415    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_B6_I.doc, keep(Zeng_all0_invdist_P)  se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_B6_I.doc
dir : seeout

. 
. 
.  
.  
.  
.  
.  
.  
.  
. 
end of do-file

. 
. 
. ******* Table B.6. II. The Battle of Three Rivers vs. Other Battles in 1858
. 
. 
. do Programs\Appendix_Table_B6_II.do

. 
. ********************************************************************************
. ***** Table B.6. II. The Battle of Three Rivers vs. Other Battles in 1858
. ***** Sample: Hunan counties, 1858
. ********************************************************************************
.  
. 
. use Data\ThreeRivers.dta,clear

. 
. 
. foreach x of varlist martyrs_battle martyrs_battle_sanhe martyrs_battle_nosanhe {
  2. gen ln`x'1=ln(1+`x')
  3. }

. 
. 
. 
. ********************************************************************************
. 
. 
. 
. sum  martyrs_battle martyrs_battle_sanhe martyrs_battle_nosanhe if year==1858

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
martyrs_b~le |         75        57.8    441.4756          0       3823
marty~_sanhe |         75          30    245.6708          0       2128
marty~osanhe |         75        27.8    196.2692          0       1695

. 
. sum  martyrs_battle martyrs_battle_sanhe martyrs_battle_nosanhe if year==1858

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
martyrs_b~le |         75        57.8    441.4756          0       3823
marty~_sanhe |         75          30    245.6708          0       2128
marty~osanhe |         75        27.8    196.2692          0       1695

. 
. 
. 
. ********************************************************************************
. ****************************** Appendix Table B.6.II San He 
. 
. 
. 
. egen  prefidXyear=group(prefid year)

. 
. 
.  reghdfe    lnmartyrs_battle1 Zeng_all0_invdist ///
>  capital    lnjinshi   lnquotas ///
>   route1    dist_nanjing ///
> lnurbanpop dist2canal lnrice lnwheat     mainriv lnhh lnarea   if year==1858 , absorb(prefid)  cluster(  cnty
> id)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         74
Absorbing 1 HDFE group                            F(  13,     47) =       2.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0238
                                                  R-squared       =     0.5961
                                                  Adj R-squared   =     0.3726
                                                  Within R-sq.    =     0.3837
Number of clusters (cntyid)  =         74         Root MSE        =     1.1786

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
lnmartyrs_battle1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_invdist |   .4136705   .1602275     2.58   0.013     .0913343    .7360067
          capital |  -1.133464   .7912437    -1.43   0.159    -2.725241    .4583133
         lnjinshi |  -.0541318    .185114    -0.29   0.771    -.4265331    .3182695
         lnquotas |  -.7401019    .932581    -0.79   0.431    -2.616213    1.136009
           route1 |   -.423007   .3990323    -1.06   0.295    -1.225756    .3797425
     dist_nanjing |   .4040011   1.397782     0.29   0.774    -2.407974    3.215977
       lnurbanpop |   .3830438   .1954918     1.96   0.056     -.010235    .7763226
       dist2canal |  -.4924228   1.382293    -0.36   0.723    -3.273237    2.288392
           lnrice |   .4686623   1.907794     0.25   0.807    -3.369324    4.306649
          lnwheat |   2.181749   3.083222     0.71   0.483    -4.020895    8.384392
          mainriv |  -.1561226   .4527382    -0.34   0.732    -1.066914    .7546691
             lnhh |    .162394   .5311665     0.31   0.761    -.9061752    1.230963
           lnarea |   .4439511   .4222046     1.05   0.298    -.4054151    1.293317
            _cons |  -9.286253   11.19316    -0.83   0.411    -31.80399    13.23148
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B6_II.doc, keep(Zeng_all0_invdist)  se  bdec(3) rdec(3) nocons addtext(O
> bservations, `e(N_full)') noobs  replace
Results\Appendix_Table_B6_II.doc
dir : seeout

. 
. 
.  reghdfe    lnmartyrs_battle_sanhe1 Zeng_all0_invdist ///
>  capital    lnjinshi   lnquotas ///
>   route1    dist_nanjing ///
> lnurbanpop  dist2canal lnrice lnwheat     mainriv lnhh lnarea if year==1858 , absorb(prefid)   cluster(  cnty
> id)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         74
Absorbing 1 HDFE group                            F(  13,     47) =       2.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0273
                                                  R-squared       =     0.5634
                                                  Adj R-squared   =     0.3219
                                                  Within R-sq.    =     0.3902
Number of clusters (cntyid)  =         74         Root MSE        =     0.9399

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
lnmartyrs~_sanhe1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_invdist |   .3973124    .152708     2.60   0.012     .0901036    .7045212
          capital |  -.9692705   .7387127    -1.31   0.196    -2.455369    .5168278
         lnjinshi |  -.1210084   .1404155    -0.86   0.393     -.403488    .1614712
         lnquotas |  -.5173868   .6379376    -0.81   0.421    -1.800752    .7659781
           route1 |  -.1585644   .3593367    -0.44   0.661    -.8814567    .5643279
     dist_nanjing |   .9057454   1.120405     0.81   0.423    -1.348219     3.15971
       lnurbanpop |   .0988435   .1593637     0.62   0.538     -.221755    .4194419
       dist2canal |  -.8691415   1.062555    -0.82   0.418    -3.006726    1.268443
           lnrice |   1.054629   1.185697     0.89   0.378    -1.330685    3.439942
          lnwheat |   2.159153   2.075745     1.04   0.304    -2.016707    6.335014
          mainriv |   -.292809   .3552501    -0.82   0.414     -1.00748    .4218619
             lnhh |   .5937728   .4451173     1.33   0.189    -.3016876    1.489233
           lnarea |   .0663601   .2946418     0.23   0.823    -.5263828     .659103
            _cons |  -11.38055   10.43951    -1.09   0.281    -32.38213    9.621033
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B6_II.doc, keep(Zeng_all0_invdist)  se  bdec(3) rdec(3) nocons addtext(O
> bservations, `e(N_full)') noobs  append
Results\Appendix_Table_B6_II.doc
dir : seeout

. 
. 
.  reghdfe    lnmartyrs_battle_nosanhe1 Zeng_all0_invdist ///
>  capital    lnjinshi   lnquotas ///
>   route1    dist_nanjing ///
> lnurbanpop  dist2canal lnrice lnwheat     mainriv lnhh lnarea  if year==1858 , absorb(prefid)   cluster(  cnt
> yid)
(dropped 1 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         74
Absorbing 1 HDFE group                            F(  13,     47) =       2.25
Statistics robust to heteroskedasticity           Prob > F        =     0.0216
                                                  R-squared       =     0.5863
                                                  Adj R-squared   =     0.3575
                                                  Within R-sq.    =     0.3984
Number of clusters (cntyid)  =         74         Root MSE        =     1.0866

                                     (Std. Err. adjusted for 74 clusters in cntyid)
-----------------------------------------------------------------------------------
                  |               Robust
lnmartyrs~osanhe1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
Zeng_all0_invdist |   .3865703   .1483679     2.61   0.012     .0880926     .685048
          capital |  -1.030616   .7284554    -1.41   0.164     -2.49608    .4348469
         lnjinshi |   .0243087    .167123     0.15   0.885    -.3118993    .3605167
         lnquotas |   -.686444   .8693134    -0.79   0.434    -2.435277    1.062389
           route1 |   -.374681   .3652771    -1.03   0.310    -1.109524    .3601617
     dist_nanjing |   .2826188   1.211906     0.23   0.817    -2.155422     2.72066
       lnurbanpop |   .3464297   .1757243     1.97   0.055     -.007082    .6999413
       dist2canal |  -.2195473   1.195357    -0.18   0.855    -2.624295    2.185201
           lnrice |   .2765505   1.809452     0.15   0.879    -3.363598    3.916699
          lnwheat |    1.97111   2.974223     0.66   0.511    -4.012256    7.954475
          mainriv |  -.1364291   .4066875    -0.34   0.739    -.9545789    .6817207
             lnhh |   .0904687   .5014983     0.18   0.858    -.9184157    1.099353
           lnarea |    .457797   .4100736     1.12   0.270    -.3671647    1.282759
            _cons |   -9.30347   10.34895    -0.90   0.373    -30.12286    11.51592
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      prefid |        14           0          14     |
-----------------------------------------------------+

. outreg2 using Results\Appendix_Table_B6_II.doc, keep(Zeng_all0_invdist)  se  bdec(3) rdec(3) nocons addtext(O
> bservations, `e(N_full)') noobs  append
Results\Appendix_Table_B6_II.doc
dir : seeout

. 
. 
. 
. 
end of do-file

. 
. 
. ******** Table 5. The Impact of Elite Connections on Elite Power: DD and DDD Estimates
. 
. 
. do Programs\Table_5.do

. 
. *********************************************************************************************************
. **********  Table 5： The Impact of Elite Connections on National Offices: DDD Estimates
. *********************************************************************************************************
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. keep if year>=1820
(32,920 observations deleted)

. 
. gen nhXZenghu_all_invdist=nonhunan*Zenghu_all_invdist

. gen hXZenghu_all_invdist=hunan*Zenghu_all_invdist

. 
. gen nhXZeng_all0_invdist=nonhunan*Zeng_all0_invdist

. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. 
. foreach x of varlist hunan  Zenghu_all_invdist   Zeng_all0_invdist Zeng_all0_invdist_pc  invdist0_L1 invdist0
> _F1    Zeng_exam0_invdist  Zeng_Extraexam_invdist   Zeng_BMF_invdist    Zeng_juren0_invdist  nhXZenghu_all_in
> vdist nhXZeng_all0_invdist  hXZenghu_all_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing  Taiping_route1 {
  2. gen h`x'Xperiod=hunan*`x'*period
  3. }

. 
. 
. 
. ********************************************************************************
. 
. 
. ***************
. reghdfe alloff      Zeng_all0_invdistXperiod   if hunan==1, absorb(year samcntyid) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      6,825
Absorbing 2 HDFE groups                           F(   1,     14) =     139.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3384
                                                  Adj R-squared   =     0.3219
                                                  Within R-sq.    =     0.0108
Number of clusters (prefid)  =         15         Root MSE        =     0.6315

                                            (Std. Err. adjusted for 15 clusters in prefid)
------------------------------------------------------------------------------------------
                         |               Robust
                  alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
Zeng_all0_invdistXperiod |    .053128   .0045011    11.80   0.000     .0434741    .0627818
                   _cons |    .128555   .0034709    37.04   0.000     .1211105    .1359994
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons replace 
Results\Table_5.doc
dir : seeout

. 
. 
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod     Zeng_all0_invdistXperiod if hunan==1, absorb(ye
> ar samcntyid) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      6,825
Absorbing 2 HDFE groups                           F(  13,     14) =      69.36
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3567
                                                  Adj R-squared   =     0.3394
                                                  Within R-sq.    =     0.0381
Number of clusters (prefid)  =         15         Root MSE        =     0.6232

                                            (Std. Err. adjusted for 15 clusters in prefid)
------------------------------------------------------------------------------------------
                         |               Robust
                  alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
       lnurbanpopXperiod |   .0755757   .0305012     2.48   0.027     .0101571    .1409943
          prefcapXperiod |  -.1975384   .0899426    -2.20   0.045    -.3904461   -.0046308
         lnjinshiXperiod |   .0559136   .0751179     0.74   0.469    -.1051983    .2170254
     lncntyquota0Xperiod |   -.311715   .2288333    -1.36   0.195    -.8025135    .1790836
        lncntypopXperiod |   .1460518    .139243     1.05   0.312    -.1525947    .4446982
       lncntyareaXperiod |   .2207809   .2482261     0.89   0.389    -.3116112    .7531729
          mainrivXperiod |  -.1856066   .1241062    -1.50   0.157     -.451788    .0805747
       dist2canalXperiod |  -.0515979   .1235011    -0.42   0.682    -.3164815    .2132856
           lnriceXperiod |  -.6585791   .2675328    -2.46   0.027     -1.23238   -.0847784
          lnwheatXperiod |   1.291742   1.206673     1.07   0.303    -1.296314    3.879798
     dist_nanjingXperiod |   .0733955   .1406133     0.52   0.610      -.22819    .3749809
   Taiping_route1Xperiod |  -.2012115   .1182064    -1.70   0.111    -.4547392    .0523161
Zeng_all0_invdistXperiod |   .0541379   .0052856    10.24   0.000     .0428013    .0654744
                   _cons |  -2.629557   2.272275    -1.16   0.267    -7.503103    2.243989
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |        75          75           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons append
Results\Table_5.doc
dir : seeout

. 
.   
. ***************
. reghdfe alloff      Zeng_all0_invdistXperiod   if hunan==0, absorb(year samcntyid) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    142,961
Absorbing 2 HDFE groups                           F(   1,    239) =       0.59
Statistics robust to heteroskedasticity           Prob > F        =     0.4417
                                                  R-squared       =     0.3876
                                                  Adj R-squared   =     0.3804
                                                  Within R-sq.    =     0.0004
Number of clusters (prefid)  =        240         Root MSE        =     0.4147

                                           (Std. Err. adjusted for 240 clusters in prefid)
------------------------------------------------------------------------------------------
                         |               Robust
                  alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
Zeng_all0_invdistXperiod |   .0089846   .0116593     0.77   0.442    -.0139835    .0319527
                   _cons |   .0858435    .004754    18.06   0.000     .0764783    .0952087
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1571        1571           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons append
Results\Table_5.doc
dir : seeout

. 
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod     Zeng_all0_invdistXperiod if hunan==0, absorb(ye
> ar samcntyid) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    142,961
Absorbing 2 HDFE groups                           F(  13,    239) =       1.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0895
                                                  R-squared       =     0.3884
                                                  Adj R-squared   =     0.3811
                                                  Within R-sq.    =     0.0017
Number of clusters (prefid)  =        240         Root MSE        =     0.4144

                                           (Std. Err. adjusted for 240 clusters in prefid)
------------------------------------------------------------------------------------------
                         |               Robust
                  alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
       lnurbanpopXperiod |   .0070552   .0034598     2.04   0.043     .0002397    .0138708
          prefcapXperiod |   .0211325   .0359006     0.59   0.557    -.0495895    .0918546
         lnjinshiXperiod |  -.0221153   .0112595    -1.96   0.051    -.0442959    .0000652
     lncntyquota0Xperiod |  -.0104254   .0224622    -0.46   0.643    -.0546746    .0338239
        lncntypopXperiod |   .0205821    .016782     1.23   0.221    -.0124773    .0536416
       lncntyareaXperiod |  -.0113785   .0152017    -0.75   0.455    -.0413249     .018568
          mainrivXperiod |  -.0084447   .0204426    -0.41   0.680    -.0487154    .0318259
       dist2canalXperiod |   .0045644   .0075734     0.60   0.547    -.0103548    .0194836
           lnriceXperiod |  -.0122202    .044042    -0.28   0.782    -.0989803    .0745399
          lnwheatXperiod |  -.0534921   .0325312    -1.64   0.101    -.1175765    .0105923
     dist_nanjingXperiod |  -.0045538   .0098266    -0.46   0.643    -.0239116    .0148039
   Taiping_route1Xperiod |  -.0175596   .0707732    -0.25   0.804    -.1569785    .1218594
Zeng_all0_invdistXperiod |   .0109726   .0112131     0.98   0.329    -.0111166    .0330617
                   _cons |   .0542563   .1166461     0.47   0.642    -.1755295    .2840421
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1571        1571           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons append
Results\Table_5.doc
dir : seeout

. 
. ***************
. reghdfe alloff     hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod hunanXperiod, absorb(year samcntyid
>  ) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(   3,    254) =      50.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3828
                                                  Adj R-squared   =     0.3756
                                                  Within R-sq.    =     0.0028
Number of clusters (prefid)  =        255         Root MSE        =     0.4270

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
hXZeng_all0_invdistXperiod |   .0441434   .0124357     3.55   0.000     .0196532    .0686335
  Zeng_all0_invdistXperiod |   .0089846   .0116578     0.77   0.442    -.0139736    .0319428
              hunanXperiod |   .0943657   .0580835     1.62   0.105     -.020021    .2087524
                     _cons |   .0850964    .004883    17.43   0.000       .07548    .0947128
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons append
Results\Table_5.doc
dir : seeout

. 
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0_invdistXpe
> riod hunanXperiod, absorb(year samcntyid ) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      12.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3837
                                                  Adj R-squared   =     0.3764
                                                  Within R-sq.    =     0.0042
Number of clusters (prefid)  =        255         Root MSE        =     0.4267

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0084483   .0036122     2.34   0.020     .0013346     .015562
            prefcapXperiod |   .0067073    .034765     0.19   0.847     -.061757    .0751716
           lnjinshiXperiod |  -.0195255   .0110433    -1.77   0.078    -.0412736    .0022226
       lncntyquota0Xperiod |  -.0159883   .0226108    -0.71   0.480    -.0605168    .0285403
          lncntypopXperiod |   .0244862   .0169169     1.45   0.149    -.0088291    .0578015
         lncntyareaXperiod |  -.0077986   .0154741    -0.50   0.615    -.0382725    .0226754
            mainrivXperiod |  -.0134493   .0203029    -0.66   0.508    -.0534327    .0265341
         dist2canalXperiod |     .00501   .0074927     0.67   0.504    -.0097458    .0197658
             lnriceXperiod |  -.0115836    .043897    -0.26   0.792    -.0980321    .0748649
            lnwheatXperiod |  -.0542949   .0323287    -1.68   0.094    -.1179614    .0093715
       dist_nanjingXperiod |  -.0048692   .0097139    -0.50   0.617    -.0239992    .0142608
     Taiping_route1Xperiod |  -.0603583   .0626488    -0.96   0.336    -.1837356     .063019
hXZeng_all0_invdistXperiod |   .0490748    .012661     3.88   0.000     .0241409    .0740086
  Zeng_all0_invdistXperiod |   .0109291   .0111462     0.98   0.328    -.0110216    .0328798
              hunanXperiod |   .0819912   .0632705     1.30   0.196    -.0426104    .2065928
                     _cons |    .010462   .1190495     0.09   0.930    -.2239878    .2449118
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Table_5.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod  hunanXperiod
> )  se  bdec(3) rdec(3) nocons append  
Results\Table_5.doc
dir : seeout

. 
. 
. 
.  
.  
.  
.       
. 
. 
end of do-file

. 
. 
. ******** Figure 5. Motivational Evidence for the Power Effect: National-level Offices by Connection-Province
. 
. 
. do Programs\Figure_5.do

. 
. *********************************************************************************************************
. ***************** Figure 5: Motivational Evidence for the Power Effect
. *********************************************************************************************************
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. 
. gen connect=(Zenghu_all_invdist>0)

. gen death=(martyrs_tot_post>0)

. 
. table hunan connect

----------------------------
          |     connect     
    Hunan |       0        1
----------+-----------------
        0 | 100,011   74,370
        1 |   4,107    4,218
----------------------------

. gen hunanXconnect=1

. replace hunanXconnect=4 if hunan==1&connect==1
(4,218 real changes made)

. replace hunanXconnect=3 if hunan==0&connect==1
(74,370 real changes made)

. replace hunanXconnect=2 if hunan==1&connect==0
(4,107 real changes made)

. table year hunanXconnect

----------------------------------
          |     hunanXconnect     
     Year |    1     2     3     4
----------+-----------------------
     1800 |  901    37   670    38
     1801 |  901    37   670    38
     1802 |  901    37   670    38
     1803 |  901    37   670    38
     1804 |  901    37   670    38
     1805 |  901    37   670    38
     1806 |  901    37   670    38
     1807 |  901    37   670    38
     1808 |  901    37   670    38
     1809 |  901    37   670    38
     1810 |  901    37   670    38
     1811 |  901    37   670    38
     1812 |  901    37   670    38
     1813 |  901    37   670    38
     1814 |  901    37   670    38
     1815 |  901    37   670    38
     1816 |  901    37   670    38
     1817 |  901    37   670    38
     1818 |  901    37   670    38
     1819 |  901    37   670    38
     1820 |  901    37   670    38
     1821 |  901    37   670    38
     1822 |  901    37   670    38
     1823 |  901    37   670    38
     1824 |  901    37   670    38
     1825 |  901    37   670    38
     1826 |  901    37   670    38
     1827 |  901    37   670    38
     1828 |  901    37   670    38
     1829 |  901    37   670    38
     1830 |  901    37   670    38
     1831 |  901    37   670    38
     1832 |  901    37   670    38
     1833 |  901    37   670    38
     1834 |  901    37   670    38
     1835 |  901    37   670    38
     1836 |  901    37   670    38
     1837 |  901    37   670    38
     1838 |  901    37   670    38
     1839 |  901    37   670    38
     1840 |  901    37   670    38
     1841 |  901    37   670    38
     1842 |  901    37   670    38
     1843 |  901    37   670    38
     1844 |  901    37   670    38
     1845 |  901    37   670    38
     1846 |  901    37   670    38
     1847 |  901    37   670    38
     1848 |  901    37   670    38
     1849 |  901    37   670    38
     1850 |  901    37   670    38
     1851 |  901    37   670    38
     1852 |  901    37   670    38
     1853 |  901    37   670    38
     1854 |  901    37   670    38
     1855 |  901    37   670    38
     1856 |  901    37   670    38
     1857 |  901    37   670    38
     1858 |  901    37   670    38
     1859 |  901    37   670    38
     1860 |  901    37   670    38
     1861 |  901    37   670    38
     1862 |  901    37   670    38
     1863 |  901    37   670    38
     1864 |  901    37   670    38
     1865 |  901    37   670    38
     1866 |  901    37   670    38
     1867 |  901    37   670    38
     1868 |  901    37   670    38
     1869 |  901    37   670    38
     1870 |  901    37   670    38
     1871 |  901    37   670    38
     1872 |  901    37   670    38
     1873 |  901    37   670    38
     1874 |  901    37   670    38
     1875 |  901    37   670    38
     1876 |  901    37   670    38
     1877 |  901    37   670    38
     1878 |  901    37   670    38
     1879 |  901    37   670    38
     1880 |  901    37   670    38
     1881 |  901    37   670    38
     1882 |  901    37   670    38
     1883 |  901    37   670    38
     1884 |  901    37   670    38
     1885 |  901    37   670    38
     1886 |  901    37   670    38
     1887 |  901    37   670    38
     1888 |  901    37   670    38
     1889 |  901    37   670    38
     1890 |  901    37   670    38
     1891 |  901    37   670    38
     1892 |  901    37   670    38
     1893 |  901    37   670    38
     1894 |  901    37   670    38
     1895 |  901    37   670    38
     1896 |  901    37   670    38
     1897 |  901    37   670    38
     1898 |  901    37   670    38
     1899 |  901    37   670    38
     1900 |  901    37   670    38
     1901 |  901    37   670    38
     1902 |  901    37   670    38
     1903 |  901    37   670    38
     1904 |  901    37   670    38
     1905 |  901    37   670    38
     1906 |  901    37   670    38
     1907 |  901    37   670    38
     1908 |  901    37   670    38
     1909 |  901    37   670    38
     1910 |  901    37   670    38
----------------------------------

. 
. keep if year>=1820
(32,920 observations deleted)

. 
. ****************************************************************************************  
. preserve

. tabout year hunanXconnect using Results\Official_HunanXconn_1800_1910.txt, replace  cells(mean alloff) format
> (3)  sum

Table output written to: Results\Official_HunanXconn_1800_1910.txt

        hunanXconnect                           
Year    1       2       3       4       Total
        Mean alloff     Mean alloff     Mean alloff     Mean alloff     Mean alloff
1820    0.018   0.000   0.169   0.053   0.080
1821    0.019   0.000   0.196   0.158   0.094
1822    0.021   0.000   0.224   0.289   0.109
1823    0.014   0.000   0.154   0.184   0.075
1824    0.020   0.000   0.134   0.158   0.069
1825    0.021   0.027   0.166   0.132   0.083
1826    0.017   0.054   0.137   0.105   0.069
1827    0.014   0.000   0.134   0.132   0.066
1828    0.013   0.000   0.160   0.184   0.077
1829    0.013   0.000   0.125   0.079   0.060
1830    0.016   0.000   0.146   0.132   0.071
1831    0.017   0.000   0.200   0.158   0.094
1832    0.022   0.000   0.213   0.079   0.101
1833    0.016   0.000   0.187   0.158   0.088
1834    0.011   0.000   0.197   0.158   0.090
1835    0.012   0.000   0.209   0.158   0.095
1836    0.016   0.000   0.185   0.053   0.085
1837    0.028   0.000   0.188   0.053   0.093
1838    0.028   0.000   0.172   0.053   0.086
1839    0.033   0.000   0.197   0.158   0.102
1840    0.037   0.000   0.216   0.105   0.111
1841    0.020   0.000   0.178   0.132   0.086
1842    0.021   0.000   0.160   0.079   0.078
1843    0.023   0.000   0.225   0.105   0.107
1844    0.020   0.000   0.197   0.132   0.094
1845    0.017   0.000   0.181   0.105   0.085
1846    0.011   0.000   0.210   0.105   0.094
1847    0.016   0.000   0.182   0.158   0.086
1848    0.011   0.000   0.188   0.132   0.086
1849    0.012   0.000   0.243   0.263   0.112
1850    0.013   0.000   0.173   0.105   0.080
1851    0.014   0.027   0.182   0.105   0.085
1852    0.011   0.027   0.222   0.289   0.104
1853    0.013   0.081   0.191   0.184   0.091
1854    0.008   0.081   0.182   0.158   0.084
1855    0.012   0.054   0.161   0.158   0.077
1856    0.007   0.054   0.155   0.132   0.071
1857    0.004   0.054   0.127   0.132   0.058
1858    0.010   0.054   0.161   0.079   0.074
1859    0.013   0.027   0.170   0.158   0.081
1860    0.013   0.000   0.181   0.211   0.086
1861    0.016   0.000   0.158   0.474   0.084
1862    0.016   0.000   0.191   0.684   0.102
1863    0.020   0.000   0.187   0.737   0.104
1864    0.020   0.054   0.181   0.605   0.100
1865    0.016   0.081   0.157   0.605   0.088
1866    0.020   0.081   0.163   0.711   0.095
1867    0.023   0.054   0.201   0.632   0.111
1868    0.019   0.027   0.163   0.605   0.091
1869    0.016   0.027   0.145   0.474   0.079
1870    0.024   0.027   0.182   0.395   0.097
1871    0.017   0.027   0.157   0.342   0.081
1872    0.020   0.027   0.131   0.342   0.073
1873    0.023   0.000   0.158   0.316   0.084
1874    0.018   0.000   0.128   0.368   0.070
1875    0.028   0.000   0.160   0.605   0.094
1876    0.030   0.000   0.181   0.447   0.100
1877    0.016   0.000   0.139   0.421   0.075
1878    0.016   0.000   0.145   0.553   0.080
1879    0.024   0.000   0.203   0.605   0.110
1880    0.020   0.027   0.130   0.553   0.077
1881    0.028   0.027   0.164   0.737   0.100
1882    0.031   0.054   0.194   0.526   0.109
1883    0.027   0.054   0.182   0.579   0.103
1884    0.026   0.054   0.173   0.632   0.100
1885    0.040   0.135   0.215   0.579   0.126
1886    0.020   0.081   0.187   0.500   0.100
1887    0.019   0.081   0.148   0.553   0.085
1888    0.031   0.054   0.185   0.632   0.108
1889    0.033   0.081   0.207   0.526   0.117
1890    0.019   0.081   0.184   0.474   0.098
1891    0.023   0.081   0.200   0.395   0.105
1892    0.020   0.081   0.158   0.368   0.086
1893    0.017   0.081   0.179   0.421   0.094
1894    0.028   0.027   0.210   0.447   0.112
1895    0.031   0.027   0.179   0.526   0.103
1896    0.018   0.000   0.142   0.342   0.075
1897    0.019   0.000   0.191   0.368   0.097
1898    0.032   0.000   0.173   0.368   0.097
1899    0.026   0.000   0.130   0.316   0.074
1900    0.037   0.000   0.206   0.368   0.112
1901    0.040   0.000   0.207   0.316   0.114
1902    0.044   0.000   0.149   0.368   0.094
1903    0.058   0.000   0.201   0.316   0.121
1904    0.043   0.000   0.121   0.263   0.079
1905    0.046   0.000   0.158   0.211   0.094
1906    0.061   0.000   0.269   0.211   0.148
1907    0.059   0.000   0.243   0.237   0.137
1908    0.053   0.027   0.233   0.316   0.132
1909    0.054   0.027   0.206   0.237   0.120
1910    0.051   0.000   0.231   0.211   0.127
Total   0.023   0.022   0.179   0.313   0.093

. 
. clear

. import delimited "Results\Official_HunanXconn_1800_1910.txt"
(6 vars, 95 obs)

. drop in 1/3
(3 observations deleted)

. drop in 92
(1 observation deleted)

. sum

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
          v1 |          0
          v2 |          0
          v3 |          0
          v4 |          0
          v5 |          0
-------------+---------------------------------------------------------
          v6 |          0

. destring v1, force gen(year)
v1: all characters numeric; year generated as int

. destring v2, force gen(UnXnonHunan)
v2: all characters numeric; UnXnonHunan generated as double

. destring v3, force gen(UnXHunan)
v3: all characters numeric; UnXHunan generated as double

. destring v4, force gen(ConnXnonHunan)
v4: all characters numeric; ConnXnonHunan generated as double

. destring v5, force gen(ConnXHunan)
v5: all characters numeric; ConnXHunan generated as double

. 
. 
. 
. drop v1 v2 v3 v4 v5 v6

. sort year

. 
. twoway (connect ConnXHunan  year, m(O) msize(vsmall) mc(gs6) lp(solid) lc(gs6) ylabel(0(0.2)0.8) ) ///
> (connect ConnXnonHunan  year, m(Oh) msize(vsmall) mc(gs6) lp(dash) lc(gs6)   ) ///
> (line UnXHunan year,  lp(solid) lc(gs6) xlabel(1820(10)1910) )  ///
>  (line UnXnonHunan year,  lp(dash) lc(gs6) xsize(8) ysize(6) xline(1850, lc(blue) lp(dash))  xline(1853, lc(b
> lue) lp(solid))   xline(1864, lc(blue) lp(dash))   graphregion(color(white) ifcolor(white) ilcolor(white) fco
> lor(white)) xtitle(Year) ytitle(Number of natioinal-offices) legend(order(1 "Connected, Hunan (38)" 2 "Connec
> ted, non-Hunan (670)" 3 "Unconnected, Hunan (37)" 4 "Unconnected, non-Hunan (901)") row(2) size(small)) title
> ("Number of national-offices" "Connected & unconnected counties in Hunan and non-Hunan", size(median)) )
(note:  named style median not found in class gsize, default attributes used)

. 
.  
. graph export Results\Figure_5.png, replace 
(file Results\Figure_5.png written in PNG format)

.  
. 
. 
. 
end of do-file

. 
. 
. ******** Table 6. The Power Effect: the Role of Soldier Deaths
. 
. 
. do Programs\Table_6.do

. 
. *********************************************************************************
. *************************** Table 6. The Power Effect: the Role of Soldier Deaths 
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. gen hXZeng_exam0_invdist=hunan*Zeng_exam0_invdist

. gen hXZeng_Extraexam_invdist=hunan*Zeng_Extraexam_invdist

. 
. 
. foreach x of varlist hunan  Zeng_all0_invdist Zenghu_all_invdist Zeng_exam0_invdist Zeng_Extraexam_invdist hX
> Zeng_all0_invdist hXZeng_exam0_invdist hXZeng_Extraexam_invdist martyrs_tot_post{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. ********************************************************************************
. 
. keep if year > =1820
(32,920 observations deleted)

. 
. 
. 
. *******
. 
. ***************  ***************  ***************  ***************
. ***************  ***************  ***************  *************** DDD Estimates, IV estimates, and Overid
. ***************  ***************  ***************  ***************
. 
. *********
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0_invdistXpe
> riod hunanXperiod, absorb(year samcntyid )  cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      12.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3837
                                                  Adj R-squared   =     0.3764
                                                  Within R-sq.    =     0.0042
Number of clusters (prefid)  =        255         Root MSE        =     0.4267

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0084483   .0036122     2.34   0.020     .0013346     .015562
            prefcapXperiod |   .0067073    .034765     0.19   0.847     -.061757    .0751716
           lnjinshiXperiod |  -.0195255   .0110433    -1.77   0.078    -.0412736    .0022226
       lncntyquota0Xperiod |  -.0159883   .0226108    -0.71   0.480    -.0605168    .0285403
          lncntypopXperiod |   .0244862   .0169169     1.45   0.149    -.0088291    .0578015
         lncntyareaXperiod |  -.0077986   .0154741    -0.50   0.615    -.0382725    .0226754
            mainrivXperiod |  -.0134493   .0203029    -0.66   0.508    -.0534327    .0265341
         dist2canalXperiod |     .00501   .0074927     0.67   0.504    -.0097458    .0197658
             lnriceXperiod |  -.0115836    .043897    -0.26   0.792    -.0980321    .0748649
            lnwheatXperiod |  -.0542949   .0323287    -1.68   0.094    -.1179614    .0093715
       dist_nanjingXperiod |  -.0048692   .0097139    -0.50   0.617    -.0239992    .0142608
     Taiping_route1Xperiod |  -.0603583   .0626488    -0.96   0.336    -.1837356     .063019
hXZeng_all0_invdistXperiod |   .0490748    .012661     3.88   0.000     .0241409    .0740086
  Zeng_all0_invdistXperiod |   .0109291   .0111462     0.98   0.328    -.0110216    .0328798
              hunanXperiod |   .0819912   .0632705     1.30   0.196    -.0426104    .2065928
                     _cons |    .010462   .1190495     0.09   0.930    -.2239878    .2449118
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod hunanXperiod)
>   se  bdec(3) rdec(3) nocons replace 
Results\Table_6.doc
dir : seeout

. 
. 
. *********
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod martyrs_tot_postXperiod    Zeng_all0_invdistXperiod
>  hunanXperiod, absorb(year samcntyid )  cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =       3.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.3924
                                                  Adj R-squared   =     0.3852
                                                  Within R-sq.    =     0.0183
Number of clusters (prefid)  =        255         Root MSE        =     0.4236

                                           (Std. Err. adjusted for 255 clusters in prefid)
------------------------------------------------------------------------------------------
                         |               Robust
                  alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
       lnurbanpopXperiod |   .0072413   .0034343     2.11   0.036      .000478    .0140045
          prefcapXperiod |   .0154959   .0337242     0.46   0.646    -.0509187    .0819105
         lnjinshiXperiod |  -.0187775   .0109189    -1.72   0.087    -.0402807    .0027257
     lncntyquota0Xperiod |  -.0159528   .0221594    -0.72   0.472    -.0595924    .0276869
        lncntypopXperiod |   .0218615   .0165729     1.32   0.188    -.0107763    .0544992
       lncntyareaXperiod |  -.0091763   .0151345    -0.61   0.545    -.0389814    .0206288
          mainrivXperiod |  -.0064681   .0197124    -0.33   0.743    -.0452887    .0323526
       dist2canalXperiod |    .004226   .0075224     0.56   0.575    -.0105883    .0190402
           lnriceXperiod |  -.0095252   .0438945    -0.22   0.828    -.0959687    .0769184
          lnwheatXperiod |  -.0525996   .0322795    -1.63   0.104    -.1161693      .01097
     dist_nanjingXperiod |  -.0041549   .0097592    -0.43   0.671     -.023374    .0150643
   Taiping_route1Xperiod |  -.0240582   .0572064    -0.42   0.674    -.1367174     .088601
 martyrs_tot_postXperiod |   .4206302   .0913565     4.60   0.000     .2407175     .600543
Zeng_all0_invdistXperiod |   .0098759   .0107033     0.92   0.357    -.0112026    .0309544
            hunanXperiod |  -.0181381   .0285236    -0.64   0.525     -.074311    .0380347
                   _cons |   .0348926   .1139727     0.31   0.760    -.1895593    .2593444
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep( hXZeng_all0_invdistXperiod  martyrs_tot_postXperiod  Zeng_all0_invdi
> stXperiod hunanXperiod)  se  bdec(3) rdec(3) nocons append 
Results\Table_6.doc
dir : seeout

. 
. 
. *********
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod
>   Zeng_all0_invdistXperiod hunanXperiod, absorb(year samcntyid )  cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  16,    254) =      10.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3924
                                                  Adj R-squared   =     0.3853
                                                  Within R-sq.    =     0.0184
Number of clusters (prefid)  =        255         Root MSE        =     0.4236

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0071605   .0033829     2.12   0.035     .0004984    .0138227
            prefcapXperiod |   .0150658   .0338741     0.44   0.657    -.0516441    .0817756
           lnjinshiXperiod |  -.0184724   .0111132    -1.66   0.098     -.040358    .0034133
       lncntyquota0Xperiod |  -.0166707   .0224658    -0.74   0.459    -.0609138    .0275723
          lncntypopXperiod |    .021983   .0166072     1.32   0.187    -.0107225    .0546884
         lncntyareaXperiod |  -.0087059   .0151274    -0.58   0.565     -.038497    .0210852
            mainrivXperiod |  -.0062882    .019723    -0.32   0.750    -.0451296    .0325532
         dist2canalXperiod |   .0041418   .0075069     0.55   0.582    -.0106419    .0189255
             lnriceXperiod |  -.0100046   .0439864    -0.23   0.820    -.0966291    .0766199
            lnwheatXperiod |  -.0523635   .0323233    -1.62   0.106    -.1160194    .0112923
       dist_nanjingXperiod |  -.0040619   .0097564    -0.42   0.678    -.0232757    .0151519
     Taiping_route1Xperiod |  -.0191413   .0586188    -0.33   0.744    -.1345822    .0962996
   martyrs_tot_postXperiod |   .4270684   .0968237     4.41   0.000     .2363889     .617748
hXZeng_all0_invdistXperiod |  -.0112966   .0152666    -0.74   0.460    -.0413619    .0187687
  Zeng_all0_invdistXperiod |   .0105738   .0111996     0.94   0.346     -.011482    .0326296
              hunanXperiod |  -.0077584   .0315271    -0.25   0.806    -.0698462    .0543293
                     _cons |   .0326566   .1151446     0.28   0.777    -.1941031    .2594164
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep( hXZeng_all0_invdistXperiod  martyrs_tot_postXperiod  Zeng_all0_invdi
> stXperiod hunanXperiod)  se  bdec(3) rdec(3) nocons append 
Results\Table_6.doc
dir : seeout

. 
. 
. *********
. 
. ivreghdfe alloff  (martyrs_tot_postXperiod =     hXZeng_Extraexam_invdistXperiod hXZeng_exam0_invdistXperiod)
>  Zeng_exam0_invdistXperiod   Zeng_Extraexam_invdistXperiod      lnurbanpopXperiod-Taiping_route1Xperiod      
>  hunanXperiod , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on prefid

Number of clusters (prefid) =      255                Number of obs =   149786
                                                      F( 16,   254) =   149.60
                                                      Prob > F      =   0.0000
Total (centered) SS     =  27064.04838                Centered R2   =   0.0186
Total (uncentered) SS   =  27064.04838                Uncentered R2 =   0.0186
Residual SS             =  26561.30128                Root MSE      =    .4213

-----------------------------------------------------------------------------------------------
                              |               Robust
                       alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
      martyrs_tot_postXperiod |   .3831375   .0774553     4.95   0.000     .2306011     .535674
    Zeng_exam0_invdistXperiod |   .0055801   .0199894     0.28   0.780     -.033786    .0449462
Zeng_Extraexam_invdistXperiod |   .0219355   .0299293     0.73   0.464    -.0370056    .0808766
            lnurbanpopXperiod |   .0074546    .003464     2.15   0.032     .0006328    .0142764
               prefcapXperiod |   .0145691   .0336978     0.43   0.666    -.0517936    .0809318
              lnjinshiXperiod |  -.0202106   .0107198    -1.89   0.061    -.0413216    .0009004
          lncntyquota0Xperiod |  -.0148621   .0219159    -0.68   0.498    -.0580221     .028298
             lncntypopXperiod |   .0210125   .0164291     1.28   0.202    -.0113422    .0533672
            lncntyareaXperiod |  -.0092391   .0152533    -0.61   0.545    -.0392781       .0208
               mainrivXperiod |  -.0075807   .0193521    -0.39   0.696    -.0456917    .0305303
            dist2canalXperiod |   .0043542   .0075656     0.58   0.565    -.0105452    .0192535
                lnriceXperiod |  -.0100419   .0440414    -0.23   0.820    -.0967746    .0766909
               lnwheatXperiod |  -.0519833   .0323135    -1.61   0.109    -.1156199    .0116533
          dist_nanjingXperiod |  -.0043651   .0097846    -0.45   0.656    -.0236344    .0149042
        Taiping_route1Xperiod |  -.0293065    .059862    -0.49   0.625    -.1471955    .0885825
                 hunanXperiod |  -.0201263   .0395119    -0.51   0.611    -.0979391    .0576864
-----------------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             37.669
                                                   Chi-sq(2) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              5.6e+04
                         (Kleibergen-Paap rk Wald F statistic):        312.255
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.055
                                                   Chi-sq(1) P-val =    0.8147
------------------------------------------------------------------------------
Instrumented:         martyrs_tot_postXperiod
Included instruments: Zeng_exam0_invdistXperiod Zeng_Extraexam_invdistXperiod
                      lnurbanpopXperiod prefcapXperiod lnjinshiXperiod
                      lncntyquota0Xperiod lncntypopXperiod lncntyareaXperiod
                      mainrivXperiod dist2canalXperiod lnriceXperiod
                      lnwheatXperiod dist_nanjingXperiod Taiping_route1Xperiod
                      hunanXperiod
Excluded instruments: hXZeng_Extraexam_invdistXperiod
                      hXZeng_exam0_invdistXperiod
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep(martyrs_tot_postXperiod  hXZeng_exam0_invdistXperiod hXZeng_Extraexam
> _invdistXperiod Zenghu_all_invdistXperiod Zenghu_all_invdistXperiod Zeng_exam0_invdistXperiod   Zeng_Extraexa
> m_invdistXperiod  hunanXperiod )  se  bdec(3) rdec(3) nocons append 
Results\Table_6.doc
dir : seeout

. 
. *********
. ivreghdfe alloff  (martyrs_tot_postXperiod =     hXZeng_Extraexam_invdistXperiod ) hXZeng_exam0_invdistXperio
> d Zeng_exam0_invdistXperiod   Zeng_Extraexam_invdistXperiod     lnurbanpopXperiod-Taiping_route1Xperiod     h
> unanXperiod  , absorb(year samcntyid) cluster(prefid)
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on prefid

Number of clusters (prefid) =      255                Number of obs =   149786
                                                      F( 17,   254) =   297.72
                                                      Prob > F      =   0.0000
Total (centered) SS     =  27064.04838                Centered R2   =   0.0186
Total (uncentered) SS   =  27064.04838                Uncentered R2 =   0.0186
Residual SS             =  26560.94652                Root MSE      =    .4213

-----------------------------------------------------------------------------------------------
                              |               Robust
                       alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
      martyrs_tot_postXperiod |   .3873619    .092614     4.18   0.000     .2049727    .5697511
  hXZeng_exam0_invdistXperiod |   .0180643   .0756785     0.24   0.812     -.130973    .1671016
    Zeng_exam0_invdistXperiod |   .0068587   .0220515     0.31   0.756    -.0365684    .0502858
Zeng_Extraexam_invdistXperiod |   .0191621   .0400542     0.48   0.633    -.0597186    .0980428
            lnurbanpopXperiod |   .0074844   .0034086     2.20   0.029     .0007717    .0141972
               prefcapXperiod |   .0149876   .0341652     0.44   0.661    -.0522956    .0822708
              lnjinshiXperiod |  -.0203719   .0108721    -1.87   0.062    -.0417829    .0010392
          lncntyquota0Xperiod |  -.0145837   .0221438    -0.66   0.511    -.0581927    .0290252
             lncntypopXperiod |   .0209677   .0165213     1.27   0.206    -.0115684    .0535039
            lncntyareaXperiod |  -.0094691    .015128    -0.63   0.532    -.0392615    .0203232
               mainrivXperiod |  -.0073853   .0192922    -0.38   0.702    -.0453784    .0306078
            dist2canalXperiod |   .0043702   .0075485     0.58   0.563    -.0104953    .0192358
                lnriceXperiod |  -.0098207   .0441686    -0.22   0.824     -.096804    .0771626
               lnwheatXperiod |    -.05209   .0322905    -1.61   0.108    -.1156812    .0115012
          dist_nanjingXperiod |  -.0043743   .0097818    -0.45   0.655    -.0236381    .0148895
        Taiping_route1Xperiod |  -.0309812    .058677    -0.53   0.598    -.1465367    .0845743
                 hunanXperiod |  -.0236792   .0381897    -0.62   0.536     -.098888    .0515295
-----------------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              3.928
                                                   Chi-sq(1) P-val =    0.0475
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              1.0e+05
                         (Kleibergen-Paap rk Wald F statistic):         80.145
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         martyrs_tot_postXperiod
Included instruments: hXZeng_exam0_invdistXperiod Zeng_exam0_invdistXperiod
                      Zeng_Extraexam_invdistXperiod lnurbanpopXperiod
                      prefcapXperiod lnjinshiXperiod lncntyquota0Xperiod
                      lncntypopXperiod lncntyareaXperiod mainrivXperiod
                      dist2canalXperiod lnriceXperiod lnwheatXperiod
                      dist_nanjingXperiod Taiping_route1Xperiod hunanXperiod
Excluded instruments: hXZeng_Extraexam_invdistXperiod
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep(martyrs_tot_postXperiod  hXZeng_exam0_invdistXperiod hXZeng_Extraexam
> _invdistXperiod Zenghu_all_invdistXperiod Zenghu_all_invdistXperiod Zeng_exam0_invdistXperiod   Zeng_Extraexa
> m_invdistXperiod  hunanXperiod)  se  bdec(3) rdec(3) nocons append 
Results\Table_6.doc
dir : seeout

. 
. *********
. ivreghdfe alloff  (martyrs_tot_postXperiod =   hXZeng_exam0_invdistXperiod)   hXZeng_Extraexam_invdistXperiod
>   Zeng_exam0_invdistXperiod   Zeng_Extraexam_invdistXperiod     hunanXperiod  lnurbanpopXperiod-Taiping_route
> 1Xperiod      , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on prefid

Number of clusters (prefid) =      255                Number of obs =   149786
                                                      F( 17,   254) =   280.37
                                                      Prob > F      =   0.0000
Total (centered) SS     =  27064.04838                Centered R2   =   0.0186
Total (uncentered) SS   =  27064.04838                Uncentered R2 =   0.0186
Residual SS             =  26561.92075                Root MSE      =    .4213

-------------------------------------------------------------------------------------------------
                                |               Robust
                         alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
        martyrs_tot_postXperiod |   .3684163   .0443213     8.31   0.000     .2811322    .4557005
hXZeng_Extraexam_invdistXperiod |   .0084423   .0352404     0.24   0.811    -.0609584     .077843
      Zeng_exam0_invdistXperiod |   .0068679   .0220709     0.31   0.756    -.0365974    .0503331
  Zeng_Extraexam_invdistXperiod |   .0191862   .0399625     0.48   0.632    -.0595138    .0978862
                   hunanXperiod |  -.0221287   .0374872    -0.59   0.556     -.095954    .0516965
              lnurbanpopXperiod |   .0075275   .0033231     2.27   0.024     .0009832    .0140717
                 prefcapXperiod |   .0145699   .0336686     0.43   0.666    -.0517352     .080875
                lnjinshiXperiod |   -.020405   .0109121    -1.87   0.063    -.0418946    .0010846
            lncntyquota0Xperiod |  -.0145752   .0221672    -0.66   0.511    -.0582301    .0290798
               lncntypopXperiod |   .0209939   .0164801     1.27   0.204    -.0114612     .053449
              lncntyareaXperiod |  -.0094174   .0151568    -0.62   0.535    -.0392664    .0204315
                 mainrivXperiod |  -.0077877   .0194599    -0.40   0.689    -.0461111    .0305356
              dist2canalXperiod |   .0043869   .0075301     0.58   0.561    -.0104424    .0192163
                  lnriceXperiod |  -.0099699   .0440804    -0.23   0.821    -.0967795    .0768397
                 lnwheatXperiod |  -.0521195   .0322851    -1.61   0.108    -.1157002    .0114611
            dist_nanjingXperiod |   -.004393   .0097721    -0.45   0.653    -.0236378    .0148517
          Taiping_route1Xperiod |  -.0311267   .0585318    -0.53   0.595    -.1463962    .0841429
-------------------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              0.992
                                                   Chi-sq(1) P-val =    0.3193
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              6.4e+04
                         (Kleibergen-Paap rk Wald F statistic):         15.365
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         martyrs_tot_postXperiod
Included instruments: hXZeng_Extraexam_invdistXperiod Zeng_exam0_invdistXperiod
                      Zeng_Extraexam_invdistXperiod hunanXperiod
                      lnurbanpopXperiod prefcapXperiod lnjinshiXperiod
                      lncntyquota0Xperiod lncntypopXperiod lncntyareaXperiod
                      mainrivXperiod dist2canalXperiod lnriceXperiod
                      lnwheatXperiod dist_nanjingXperiod Taiping_route1Xperiod
Excluded instruments: hXZeng_exam0_invdistXperiod
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Table_6.doc, keep(martyrs_tot_postXperiod  hXZeng_exam0_invdistXperiod hXZeng_Extraexam
> _invdistXperiod Zenghu_all_invdistXperiod Zenghu_all_invdistXperiod Zeng_exam0_invdistXperiod   Zeng_Extraexa
> m_invdistXperiod  hunanXperiod)  se  bdec(3) rdec(3) nocons append 
Results\Table_6.doc
dir : seeout

. 
. 
. 
. 
end of do-file

. 
. 
. ******** Figure 6. The Dynamics Impacts of Elite Network on National-level Offices
. 
. 
. do Programs\Figure_6.do

. 
. 
. *********************************************************************************************************
. **** Figure 6: The dynamic impacts of elite network on national-level office
. *********************************************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen nhXZenghu_all_invdist=nonhunan*Zenghu_all_invdist

. gen hXZenghu_all_invdist=hunan*Zenghu_all_invdist

. 
. gen nhXZeng_all0_invdist=nonhunan*Zeng_all0_invdist

. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. foreach x of varlist hunan  Zenghu_all_invdist   Zeng_all0_invdist Zeng_all0_invdist_pc  invdist0_L1 invdist0
> _F1    Zeng_exam0_invdist  Zeng_Extraexam_invdist   Zeng_BMF_invdist    Zeng_juren0_invdist  nhXZenghu_all_in
> vdist nhXZeng_all0_invdist  hXZenghu_all_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod1=`x'*period1
  3. gen `x'Xperiod2=`x'*period2
  4. gen `x'Xperiod=`x'*period
  5. 
. }

. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod1=`x'*period1
  3. gen `x'Xperiod2=`x'*period2
  4. gen `x'Xperiod=`x'*period
  5. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing  Taiping_route1 {
  2. gen h`x'Xperiod1=hunan*`x'*period1
  3. gen h`x'Xperiod2=hunan*`x'*period2
  4. gen h`x'Xperiod=hunan*`x'*period
  5. }

. 
. 
. 
. 
. ********************************************************************************
. ********** gen year dummy & interactions
. 
. 
. drop if year==.
(0 observations deleted)

. tab year, gen(year)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1800 |      1,646        0.90        0.90
       1801 |      1,646        0.90        1.80
       1802 |      1,646        0.90        2.70
       1803 |      1,646        0.90        3.60
       1804 |      1,646        0.90        4.50
       1805 |      1,646        0.90        5.41
       1806 |      1,646        0.90        6.31
       1807 |      1,646        0.90        7.21
       1808 |      1,646        0.90        8.11
       1809 |      1,646        0.90        9.01
       1810 |      1,646        0.90        9.91
       1811 |      1,646        0.90       10.81
       1812 |      1,646        0.90       11.71
       1813 |      1,646        0.90       12.61
       1814 |      1,646        0.90       13.51
       1815 |      1,646        0.90       14.41
       1816 |      1,646        0.90       15.32
       1817 |      1,646        0.90       16.22
       1818 |      1,646        0.90       17.12
       1819 |      1,646        0.90       18.02
       1820 |      1,646        0.90       18.92
       1821 |      1,646        0.90       19.82
       1822 |      1,646        0.90       20.72
       1823 |      1,646        0.90       21.62
       1824 |      1,646        0.90       22.52
       1825 |      1,646        0.90       23.42
       1826 |      1,646        0.90       24.32
       1827 |      1,646        0.90       25.23
       1828 |      1,646        0.90       26.13
       1829 |      1,646        0.90       27.03
       1830 |      1,646        0.90       27.93
       1831 |      1,646        0.90       28.83
       1832 |      1,646        0.90       29.73
       1833 |      1,646        0.90       30.63
       1834 |      1,646        0.90       31.53
       1835 |      1,646        0.90       32.43
       1836 |      1,646        0.90       33.33
       1837 |      1,646        0.90       34.23
       1838 |      1,646        0.90       35.14
       1839 |      1,646        0.90       36.04
       1840 |      1,646        0.90       36.94
       1841 |      1,646        0.90       37.84
       1842 |      1,646        0.90       38.74
       1843 |      1,646        0.90       39.64
       1844 |      1,646        0.90       40.54
       1845 |      1,646        0.90       41.44
       1846 |      1,646        0.90       42.34
       1847 |      1,646        0.90       43.24
       1848 |      1,646        0.90       44.14
       1849 |      1,646        0.90       45.05
       1850 |      1,646        0.90       45.95
       1851 |      1,646        0.90       46.85
       1852 |      1,646        0.90       47.75
       1853 |      1,646        0.90       48.65
       1854 |      1,646        0.90       49.55
       1855 |      1,646        0.90       50.45
       1856 |      1,646        0.90       51.35
       1857 |      1,646        0.90       52.25
       1858 |      1,646        0.90       53.15
       1859 |      1,646        0.90       54.05
       1860 |      1,646        0.90       54.95
       1861 |      1,646        0.90       55.86
       1862 |      1,646        0.90       56.76
       1863 |      1,646        0.90       57.66
       1864 |      1,646        0.90       58.56
       1865 |      1,646        0.90       59.46
       1866 |      1,646        0.90       60.36
       1867 |      1,646        0.90       61.26
       1868 |      1,646        0.90       62.16
       1869 |      1,646        0.90       63.06
       1870 |      1,646        0.90       63.96
       1871 |      1,646        0.90       64.86
       1872 |      1,646        0.90       65.77
       1873 |      1,646        0.90       66.67
       1874 |      1,646        0.90       67.57
       1875 |      1,646        0.90       68.47
       1876 |      1,646        0.90       69.37
       1877 |      1,646        0.90       70.27
       1878 |      1,646        0.90       71.17
       1879 |      1,646        0.90       72.07
       1880 |      1,646        0.90       72.97
       1881 |      1,646        0.90       73.87
       1882 |      1,646        0.90       74.77
       1883 |      1,646        0.90       75.68
       1884 |      1,646        0.90       76.58
       1885 |      1,646        0.90       77.48
       1886 |      1,646        0.90       78.38
       1887 |      1,646        0.90       79.28
       1888 |      1,646        0.90       80.18
       1889 |      1,646        0.90       81.08
       1890 |      1,646        0.90       81.98
       1891 |      1,646        0.90       82.88
       1892 |      1,646        0.90       83.78
       1893 |      1,646        0.90       84.68
       1894 |      1,646        0.90       85.59
       1895 |      1,646        0.90       86.49
       1896 |      1,646        0.90       87.39
       1897 |      1,646        0.90       88.29
       1898 |      1,646        0.90       89.19
       1899 |      1,646        0.90       90.09
       1900 |      1,646        0.90       90.99
       1901 |      1,646        0.90       91.89
       1902 |      1,646        0.90       92.79
       1903 |      1,646        0.90       93.69
       1904 |      1,646        0.90       94.59
       1905 |      1,646        0.90       95.50
       1906 |      1,646        0.90       96.40
       1907 |      1,646        0.90       97.30
       1908 |      1,646        0.90       98.20
       1909 |      1,646        0.90       99.10
       1910 |      1,646        0.90      100.00
------------+-----------------------------------
      Total |    182,706      100.00

. 
. 
. foreach x of varlist year2-year111 {
  2. gen hunanX`x'=hunan*`x'
  3. 
. 
. }

. 
. ***************************************************************
. foreach x of varlist year2-year111 {
  2. gen Zeng_all0_invdistX`x'=Zeng_all0_invdist*`x'
  3. }

. 
. ********
. foreach x of varlist year2-year111 {
  2. gen hXZeng_all0_invdistX`x'=hXZeng_all0_invdist*`x'
  3. }

. 
. 
. ********
. foreach x of varlist year2-year111 {
  2. gen nhXZeng_all0_invdistX`x'=nhXZeng_all0_invdist*`x'
  3. }

. 
. 
. ***************************************************************
. foreach x of varlist year2-year111 {
  2. gen Zenghu_all_invdistX`x'=Zenghu_all_invdist*`x'
  3. }

. 
. ********
. foreach x of varlist year2-year111 {
  2. gen hXZenghu_all_invdistX`x'=hXZenghu_all_invdist*`x'
  3. }

. 
. 
. ********
. foreach x of varlist year2-year111 {
  2. gen nhXZenghu_all_invdistX`x'=nhXZenghu_all_invdist*`x'
  3. }

. 
. 
. ********************************************************************************
. 
. 
. reghdfe alloff   hXZeng_all0_invdistXyear22-hXZeng_all0_invdistXyear111  nhXZeng_all0_invdistXyear22-nhXZeng_
> all0_invdistXyear111  hunanXyear22-hunanXyear111   lnurbanpopXperiod-Taiping_route1Xperiod   ,  absorb(year s
> amcntyid  ) cluster(prefid )
(MWFE estimator converged in 2 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =    182,706
Absorbing 2 HDFE groups                           F( 304,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.3779
                                                  Adj R-squared   =     0.3708
                                                  Within R-sq.    =     0.0162
Number of clusters (prefid)  =        255         Root MSE        =     0.4270

                                               (Std. Err. adjusted for 255 clusters in prefid)
----------------------------------------------------------------------------------------------
                             |               Robust
                      alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
  hXZeng_all0_invdistXyear22 |   .0160295   .0056047     2.86   0.005     .0049919     .027067
  hXZeng_all0_invdistXyear23 |   .0184789   .0190997     0.97   0.334    -.0191349    .0560928
  hXZeng_all0_invdistXyear24 |   .0158954   .0125384     1.27   0.206     -.008797    .0405879
  hXZeng_all0_invdistXyear25 |   .0104835   .0072492     1.45   0.149    -.0037927    .0247596
  hXZeng_all0_invdistXyear26 |   .0108301   .0090639     1.19   0.233    -.0070198      .02868
  hXZeng_all0_invdistXyear27 |   .0028577   .0085025     0.34   0.737    -.0138867    .0196021
  hXZeng_all0_invdistXyear28 |   .0071513   .0096829     0.74   0.461    -.0119178    .0262203
  hXZeng_all0_invdistXyear29 |   .0131224   .0105119     1.25   0.213    -.0075791     .033824
  hXZeng_all0_invdistXyear30 |   .0032598   .0031356     1.04   0.300    -.0029153    .0094349
  hXZeng_all0_invdistXyear31 |   .0085378   .0048759     1.75   0.081    -.0010645      .01814
  hXZeng_all0_invdistXyear32 |   .0032044   .0065041     0.49   0.623    -.0096046    .0160133
  hXZeng_all0_invdistXyear33 |   .0025666   .0016571     1.55   0.123    -.0006968    .0058299
  hXZeng_all0_invdistXyear34 |   .0052841   .0054864     0.96   0.336    -.0055206    .0160888
  hXZeng_all0_invdistXyear35 |    .009097   .0065045     1.40   0.163    -.0037126    .0219065
  hXZeng_all0_invdistXyear36 |   .0115233   .0039706     2.90   0.004     .0037039    .0193428
  hXZeng_all0_invdistXyear37 |   .0009675   .0020177     0.48   0.632    -.0030061     .004941
  hXZeng_all0_invdistXyear38 |   .0009675   .0020177     0.48   0.632    -.0030061     .004941
  hXZeng_all0_invdistXyear39 |   .0009675   .0020177     0.48   0.632    -.0030061     .004941
  hXZeng_all0_invdistXyear40 |   .0052841   .0054864     0.96   0.336    -.0055206    .0160888
  hXZeng_all0_invdistXyear41 |   .0083252      .0017     4.90   0.000     .0049773    .0116731
  hXZeng_all0_invdistXyear42 |   .0192831   .0019803     9.74   0.000     .0153833    .0231829
  hXZeng_all0_invdistXyear43 |   .0098457    .002513     3.92   0.000     .0048966    .0147947
  hXZeng_all0_invdistXyear44 |   .0194171   .0022615     8.59   0.000     .0149634    .0238709
  hXZeng_all0_invdistXyear45 |   .0126972   .0037454     3.39   0.001     .0053213    .0200732
  hXZeng_all0_invdistXyear46 |    .012138   .0025999     4.67   0.000      .007018     .017258
  hXZeng_all0_invdistXyear47 |   .0128313   .0024004     5.35   0.000     .0081041    .0175585
  hXZeng_all0_invdistXyear48 |   .0361337   .0021734    16.63   0.000     .0318536    .0404138
  hXZeng_all0_invdistXyear49 |   .0217095   .0052881     4.11   0.000     .0112953    .0321237
  hXZeng_all0_invdistXyear50 |   .0449565   .0134322     3.35   0.001     .0185038    .0714091
  hXZeng_all0_invdistXyear51 |   .0186483   .0080771     2.31   0.022     .0027417    .0345548
  hXZeng_all0_invdistXyear52 |   .0133149   .0056516     2.36   0.019      .002185    .0244448
  hXZeng_all0_invdistXyear53 |    .048079   .0165358     2.91   0.004     .0155142    .0806439
  hXZeng_all0_invdistXyear54 |   -.000527     .01212    -0.04   0.965    -.0243956    .0233416
  hXZeng_all0_invdistXyear55 |    .027065   .0084485     3.20   0.002      .010427    .0437029
  hXZeng_all0_invdistXyear56 |   .0240794   .0058203     4.14   0.000     .0126172    .0355416
  hXZeng_all0_invdistXyear57 |   .0148545   .0068621     2.16   0.031     .0013407    .0283683
  hXZeng_all0_invdistXyear58 |    .027333   .0077511     3.53   0.000     .0120684    .0425976
  hXZeng_all0_invdistXyear59 |   .0158158   .0084945     1.86   0.064    -.0009129    .0325445
  hXZeng_all0_invdistXyear60 |   .0252533   .0098211     2.57   0.011     .0059121    .0445944
  hXZeng_all0_invdistXyear61 |   .0226929   .0117835     1.93   0.055     -.000513    .0458987
  hXZeng_all0_invdistXyear62 |   .1159811    .007986    14.52   0.000      .100254    .1317083
  hXZeng_all0_invdistXyear63 |   .1454119   .0201905     7.20   0.000     .1056498     .185174
  hXZeng_all0_invdistXyear64 |   .1936713   .0189921    10.20   0.000     .1562692    .2310734
  hXZeng_all0_invdistXyear65 |   .1597575    .018207     8.77   0.000     .1239016    .1956134
  hXZeng_all0_invdistXyear66 |   .1348728   .0168206     8.02   0.000     .1017471    .1679985
  hXZeng_all0_invdistXyear67 |   .1852905   .0170559    10.86   0.000     .1517015    .2188795
  hXZeng_all0_invdistXyear68 |   .1442317   .0147159     9.80   0.000     .1152509    .1732124
  hXZeng_all0_invdistXyear69 |   .1607911    .008874    18.12   0.000      .143315    .1782671
  hXZeng_all0_invdistXyear70 |   .1181331   .0106133    11.13   0.000     .0972319    .1390344
  hXZeng_all0_invdistXyear71 |   .1050168   .0085219    12.32   0.000     .0882343    .1217994
  hXZeng_all0_invdistXyear72 |   .0626501   .0117244     5.34   0.000     .0395606    .0857395
  hXZeng_all0_invdistXyear73 |   .0668096   .0079671     8.39   0.000     .0511195    .0824996
  hXZeng_all0_invdistXyear74 |   .0518261   .0072211     7.18   0.000     .0376053    .0660469
  hXZeng_all0_invdistXyear75 |   .0595304    .010732     5.55   0.000     .0383953    .0806655
  hXZeng_all0_invdistXyear76 |   .0936799   .0117189     7.99   0.000     .0706013    .1167586
  hXZeng_all0_invdistXyear77 |    .076113   .0070555    10.79   0.000     .0622182    .0900077
  hXZeng_all0_invdistXyear78 |   .0693145   .0070316     9.86   0.000     .0554668    .0831622
  hXZeng_all0_invdistXyear79 |   .1206381   .0079611    15.15   0.000     .1049599    .1363163
  hXZeng_all0_invdistXyear80 |   .0864008   .0105062     8.22   0.000     .0657104    .1070912
  hXZeng_all0_invdistXyear81 |   .0938139   .0093326    10.05   0.000     .0754348    .1121931
  hXZeng_all0_invdistXyear82 |   .1282315   .0085985    14.91   0.000      .111298    .1451649
  hXZeng_all0_invdistXyear83 |   .1014397   .0091012    11.15   0.000     .0835162    .1193632
  hXZeng_all0_invdistXyear84 |   .1323678   .0084037    15.75   0.000      .115818    .1489177
  hXZeng_all0_invdistXyear85 |   .1539371   .0073193    21.03   0.000     .1395229    .1683514
  hXZeng_all0_invdistXyear86 |    .127113   .0122266    10.40   0.000     .1030346    .1511914
  hXZeng_all0_invdistXyear87 |   .0931207   .0111571     8.35   0.000     .0711484    .1150929
  hXZeng_all0_invdistXyear88 |   .1198894   .0121515     9.87   0.000     .0959589    .1438198
  hXZeng_all0_invdistXyear89 |   .1168483   .0241303     4.84   0.000     .0693273    .1643693
  hXZeng_all0_invdistXyear90 |   .0947198   .0125364     7.56   0.000     .0700313    .1194082
  hXZeng_all0_invdistXyear91 |   .0925615   .0109636     8.44   0.000     .0709704    .1141525
  hXZeng_all0_invdistXyear92 |   .0357705   .0076363     4.68   0.000      .020732    .0508089
  hXZeng_all0_invdistXyear93 |   .0355579   .0078061     4.56   0.000      .020185    .0509307
  hXZeng_all0_invdistXyear94 |   .0373696   .0114351     3.27   0.001     .0148499    .0598892
  hXZeng_all0_invdistXyear95 |   .0465158   .0149423     3.11   0.002     .0170892    .0759425
  hXZeng_all0_invdistXyear96 |   .0707241   .0166082     4.26   0.000     .0380168    .1034314
  hXZeng_all0_invdistXyear97 |    .036094   .0143744     2.51   0.013     .0077859    .0644021
  hXZeng_all0_invdistXyear98 |   .0186287   .0216968     0.86   0.391    -.0240999    .0613573
  hXZeng_all0_invdistXyear99 |   .0120428   .0144036     0.84   0.404    -.0163228    .0404085
 hXZeng_all0_invdistXyear100 |   .0175103   .0209767     0.83   0.405    -.0238001    .0588206
 hXZeng_all0_invdistXyear101 |   .0106563    .017028     0.63   0.532    -.0228776    .0441903
 hXZeng_all0_invdistXyear102 |   .0209765    .015164     1.38   0.168    -.0088867    .0508398
 hXZeng_all0_invdistXyear103 |   .0279876    .010393     2.69   0.008     .0075202     .048455
 hXZeng_all0_invdistXyear104 |   .0199366    .011764     1.69   0.091    -.0032308    .0431041
 hXZeng_all0_invdistXyear105 |   .0156986   .0085525     1.84   0.068    -.0011443    .0325414
 hXZeng_all0_invdistXyear106 |   .0114605   .0076206     1.50   0.134    -.0035472    .0264682
 hXZeng_all0_invdistXyear107 |     .01562   .0051534     3.03   0.003     .0054711    .0257689
 hXZeng_all0_invdistXyear108 |   .0248449   .0070804     3.51   0.001      .010901    .0387887
 hXZeng_all0_invdistXyear109 |   .0315879   .0082792     3.82   0.000     .0152832    .0478925
 hXZeng_all0_invdistXyear110 |   .0202047   .0068214     2.96   0.003     .0067711    .0336384
 hXZeng_all0_invdistXyear111 |    .021166   .0065631     3.23   0.001      .008241     .034091
 nhXZeng_all0_invdistXyear22 |   .0118406   .0082216     1.44   0.151    -.0043505    .0280318
 nhXZeng_all0_invdistXyear23 |  -.0136896   .0241413    -0.57   0.571    -.0612322    .0338531
 nhXZeng_all0_invdistXyear24 |  -.0436531   .0300506    -1.45   0.148    -.1028332     .015527
 nhXZeng_all0_invdistXyear25 |  -.0557394   .0334014    -1.67   0.096    -.1215185    .0100396
 nhXZeng_all0_invdistXyear26 |  -.0476218   .0355905    -1.34   0.182    -.1177118    .0224683
 nhXZeng_all0_invdistXyear27 |  -.0513079    .034914    -1.47   0.143    -.1200656    .0174498
 nhXZeng_all0_invdistXyear28 |  -.0502162   .0345924    -1.45   0.148    -.1183407    .0179082
 nhXZeng_all0_invdistXyear29 |   .0020928   .0083107     0.25   0.801    -.0142739    .0184596
 nhXZeng_all0_invdistXyear30 |  -.0400221   .0241103    -1.66   0.098    -.0875036    .0074594
 nhXZeng_all0_invdistXyear31 |  -.0286679   .0201697    -1.42   0.156    -.0683892    .0110533
 nhXZeng_all0_invdistXyear32 |  -.0263438   .0418449    -0.63   0.530     -.108751    .0560635
 nhXZeng_all0_invdistXyear33 |  -.0362246   .0519348    -0.70   0.486    -.1385022     .066053
 nhXZeng_all0_invdistXyear34 |  -.0386502   .0452732    -0.85   0.394    -.1278089    .0505085
 nhXZeng_all0_invdistXyear35 |  -.0067709   .0343096    -0.20   0.844    -.0743384    .0607967
 nhXZeng_all0_invdistXyear36 |  -.0201578   .0369223    -0.55   0.586    -.0928706    .0525549
 nhXZeng_all0_invdistXyear37 |  -.0245174    .035693    -0.69   0.493    -.0948092    .0457745
 nhXZeng_all0_invdistXyear38 |  -.0276017   .0421638    -0.65   0.513    -.1106369    .0554334
 nhXZeng_all0_invdistXyear39 |  -.0349123   .0397195    -0.88   0.380    -.1131338    .0433093
 nhXZeng_all0_invdistXyear40 |  -.0101869   .0415031    -0.25   0.806    -.0919209    .0715471
 nhXZeng_all0_invdistXyear41 |  -.0134249   .0482577    -0.28   0.781    -.1084611    .0816113
 nhXZeng_all0_invdistXyear42 |   -.041047   .0452773    -0.91   0.365    -.1302136    .0481197
 nhXZeng_all0_invdistXyear43 |  -.0359151   .0405131    -0.89   0.376    -.1156995    .0438693
 nhXZeng_all0_invdistXyear44 |  -.0063187   .0342304    -0.18   0.854    -.0737302    .0610928
 nhXZeng_all0_invdistXyear45 |  -.0144364    .040266    -0.36   0.720    -.0937341    .0648614
 nhXZeng_all0_invdistXyear46 |  -.0250061   .0512371    -0.49   0.626    -.1259099    .0758976
 nhXZeng_all0_invdistXyear47 |  -.0246457   .0509057    -0.48   0.629    -.1248967    .0756054
 nhXZeng_all0_invdistXyear48 |  -.0205183   .0399389    -0.51   0.608    -.0991718    .0581352
 nhXZeng_all0_invdistXyear49 |  -.0236087    .038305    -0.62   0.538    -.0990445    .0518271
 nhXZeng_all0_invdistXyear50 |   .0048388   .0232437     0.21   0.835    -.0409361    .0506137
 nhXZeng_all0_invdistXyear51 |  -.0299773    .025873    -1.16   0.248    -.0809302    .0209756
 nhXZeng_all0_invdistXyear52 |  -.0053168   .0144717    -0.37   0.714    -.0338165     .023183
 nhXZeng_all0_invdistXyear53 |   .0264813   .0178775     1.48   0.140    -.0087257    .0616883
 nhXZeng_all0_invdistXyear54 |  -.0103832   .0210778    -0.49   0.623    -.0518926    .0311263
 nhXZeng_all0_invdistXyear55 |  -.0242928   .0343552    -0.71   0.480    -.0919501    .0433645
 nhXZeng_all0_invdistXyear56 |  -.0114458   .0111037    -1.03   0.304    -.0333128    .0104212
 nhXZeng_all0_invdistXyear57 |  -.0254964   .0277388    -0.92   0.359    -.0801238     .029131
 nhXZeng_all0_invdistXyear58 |  -.0318379   .0254379    -1.25   0.212    -.0819341    .0182582
 nhXZeng_all0_invdistXyear59 |  -.0074467   .0130766    -0.57   0.570    -.0331991    .0183056
 nhXZeng_all0_invdistXyear60 |  -.0237092   .0216197    -1.10   0.274    -.0662859    .0188676
 nhXZeng_all0_invdistXyear61 |  -.0045577   .0136689    -0.33   0.739    -.0314765    .0223611
 nhXZeng_all0_invdistXyear62 |  -.0161998    .015301    -1.06   0.291    -.0463328    .0139331
 nhXZeng_all0_invdistXyear63 |   .0115085   .0134421     0.86   0.393    -.0149637    .0379806
 nhXZeng_all0_invdistXyear64 |   .0177187   .0112241     1.58   0.116    -.0043855     .039823
 nhXZeng_all0_invdistXyear65 |  -.0087347   .0265724    -0.33   0.743    -.0610651    .0435957
 nhXZeng_all0_invdistXyear66 |  -.0194331   .0243429    -0.80   0.425    -.0673728    .0285066
 nhXZeng_all0_invdistXyear67 |  -.0373105   .0391741    -0.95   0.342    -.1144579     .039837
 nhXZeng_all0_invdistXyear68 |   .0090925   .0204424     0.44   0.657    -.0311657    .0493507
 nhXZeng_all0_invdistXyear69 |   .0213245   .0128667     1.66   0.099    -.0040145    .0466635
 nhXZeng_all0_invdistXyear70 |  -.0060705   .0162175    -0.37   0.708    -.0380084    .0258674
 nhXZeng_all0_invdistXyear71 |   .0142218   .0193059     0.74   0.462    -.0237982    .0522418
 nhXZeng_all0_invdistXyear72 |   .0073621   .0093787     0.78   0.433    -.0111078    .0258319
 nhXZeng_all0_invdistXyear73 |  -.0070506   .0100752    -0.70   0.485    -.0268922     .012791
 nhXZeng_all0_invdistXyear74 |  -.0189829   .0170601    -1.11   0.267    -.0525802    .0146144
 nhXZeng_all0_invdistXyear75 |  -.0160095   .0078163    -2.05   0.042    -.0314025   -.0006165
 nhXZeng_all0_invdistXyear76 |   .0090317   .0139894     0.65   0.519    -.0185182    .0365816
 nhXZeng_all0_invdistXyear77 |   .0201709   .0149398     1.35   0.178    -.0092509    .0495926
 nhXZeng_all0_invdistXyear78 |  -.0005716   .0084768    -0.07   0.946    -.0172654    .0161222
 nhXZeng_all0_invdistXyear79 |   .0048132    .012109     0.40   0.691    -.0190336      .02866
 nhXZeng_all0_invdistXyear80 |   .0307037   .0257212     1.19   0.234    -.0199502    .0813576
 nhXZeng_all0_invdistXyear81 |   .0035887   .0162318     0.22   0.825    -.0283773    .0355547
 nhXZeng_all0_invdistXyear82 |  -.0039136   .0161653    -0.24   0.809    -.0357487    .0279215
 nhXZeng_all0_invdistXyear83 |  -.0262946   .0451851    -0.58   0.561    -.1152796    .0626905
 nhXZeng_all0_invdistXyear84 |  -.0319415   .0420497    -0.76   0.448    -.1147518    .0508689
 nhXZeng_all0_invdistXyear85 |  -.0342972   .0489492    -0.70   0.484    -.1306952    .0621008
 nhXZeng_all0_invdistXyear86 |  -.0054658   .0543312    -0.10   0.920    -.1124628    .1015311
 nhXZeng_all0_invdistXyear87 |  -.0151533   .0479269    -0.32   0.752    -.1095379    .0792314
 nhXZeng_all0_invdistXyear88 |  -.0353228   .0492848    -0.72   0.474    -.1323818    .0617361
 nhXZeng_all0_invdistXyear89 |  -.0195377   .0474351    -0.41   0.681     -.112954    .0738786
 nhXZeng_all0_invdistXyear90 |  -.0040694   .0470437    -0.09   0.931    -.0967149    .0885761
 nhXZeng_all0_invdistXyear91 |  -.0199443    .056649    -0.35   0.725    -.1315059    .0916173
 nhXZeng_all0_invdistXyear92 |  -.0086464    .054237    -0.16   0.873     -.115458    .0981651
 nhXZeng_all0_invdistXyear93 |  -.0406772    .054863    -0.74   0.459    -.1487216    .0673671
 nhXZeng_all0_invdistXyear94 |  -.0363228   .0482815    -0.75   0.453    -.1314059    .0587603
 nhXZeng_all0_invdistXyear95 |  -.0251188   .0518314    -0.48   0.628    -.1271928    .0769552
 nhXZeng_all0_invdistXyear96 |  -.0385204   .0554874    -0.69   0.488    -.1477943    .0707536
 nhXZeng_all0_invdistXyear97 |    -.05326   .0494527    -1.08   0.283    -.1506495    .0441295
 nhXZeng_all0_invdistXyear98 |  -.0212753   .0394386    -0.54   0.590    -.0989436     .056393
 nhXZeng_all0_invdistXyear99 |   .0038781   .0215362     0.18   0.857    -.0385342    .0462903
nhXZeng_all0_invdistXyear100 |  -.0315489   .0215457    -1.46   0.144    -.0739799    .0108821
nhXZeng_all0_invdistXyear101 |   .0024121   .0263128     0.09   0.927    -.0494069    .0542312
nhXZeng_all0_invdistXyear102 |   .0054321   .0198573     0.27   0.785    -.0336738    .0445379
nhXZeng_all0_invdistXyear103 |  -.0330316   .0367889    -0.90   0.370    -.1054818    .0394186
nhXZeng_all0_invdistXyear104 |  -.0339338   .0506304    -0.67   0.503    -.1336426     .065775
nhXZeng_all0_invdistXyear105 |  -.0556289   .0428102    -1.30   0.195    -.1399372    .0286793
nhXZeng_all0_invdistXyear106 |  -.0355131   .0421342    -0.84   0.400    -.1184899    .0474637
nhXZeng_all0_invdistXyear107 |  -.0060522   .0553207    -0.11   0.913    -.1149979    .1028936
nhXZeng_all0_invdistXyear108 |   .0174407   .0349057     0.50   0.618    -.0513007    .0861822
nhXZeng_all0_invdistXyear109 |  -.0096242   .0441321    -0.22   0.828    -.0965357    .0772873
nhXZeng_all0_invdistXyear110 |  -.0129457   .0452705    -0.29   0.775    -.1020991    .0762076
nhXZeng_all0_invdistXyear111 |   .0080777   .0459548     0.18   0.861    -.0824233    .0985787
                hunanXyear22 |   .0294538   .0306573     0.96   0.338    -.0309211    .0898287
                hunanXyear23 |   .0631183   .0546088     1.16   0.249    -.0444253    .1706619
                hunanXyear24 |   .0271968   .0393119     0.69   0.490    -.0502219    .1046155
                hunanXyear25 |   .0177507   .0402323     0.44   0.659    -.0614807    .0969822
                hunanXyear26 |   .0086045    .031583     0.27   0.786    -.0535933    .0708023
                hunanXyear27 |   .0306602   .0420757     0.73   0.467    -.0522015    .1135219
                hunanXyear28 |   .0152978   .0372209     0.41   0.681     -.058003    .0885987
                hunanXyear29 |   .0584801   .0277805     2.11   0.036     .0037707    .1131895
                hunanXyear30 |   .0045138   .0209946     0.21   0.830    -.0368318    .0458594
                hunanXyear31 |   .0218893   .0279293     0.78   0.434    -.0331133    .0768918
                hunanXyear32 |   .0197497   .0637385     0.31   0.757    -.1057736     .145273
                hunanXyear33 |  -.0348087   .0370293    -0.94   0.348    -.1077323    .0381148
                hunanXyear34 |   .0155436   .0541789     0.29   0.774    -.0911534    .1222406
                hunanXyear35 |   .0296922   .0399731     0.74   0.458    -.0490287    .1084132
                hunanXyear36 |   .0122618   .0427818     0.29   0.775    -.0719904    .0965141
                hunanXyear37 |   -.022639   .0245607    -0.92   0.358    -.0710076    .0257297
                hunanXyear38 |  -.0329218   .0274003    -1.20   0.231    -.0868825    .0210389
                hunanXyear39 |  -.0306788   .0263374    -1.16   0.245    -.0825462    .0211886
                hunanXyear40 |   .0194319   .0513128     0.38   0.705    -.0816209    .1204846
                hunanXyear41 |  -.0232711   .0329725    -0.71   0.481    -.0882055    .0416632
                hunanXyear42 |  -.0153112   .0436328    -0.35   0.726    -.1012394    .0706169
                hunanXyear43 |  -.0200168   .0279334    -0.72   0.474    -.0750274    .0349937
                hunanXyear44 |  -.0284815   .0250032    -1.14   0.256    -.0777215    .0207586
                hunanXyear45 |   .0018443   .0305676     0.06   0.952     -.058354    .0620426
                hunanXyear46 |  -.0087696   .0346436    -0.25   0.800     -.076995    .0594557
                hunanXyear47 |  -.0189365   .0338494    -0.56   0.576    -.0855977    .0477247
                hunanXyear48 |  -.0087229   .0339959    -0.26   0.798    -.0756726    .0582269
                hunanXyear49 |  -.0063101   .0309815    -0.20   0.839    -.0673234    .0547031
                hunanXyear50 |   .0260669   .0366128     0.71   0.477    -.0460364    .0981702
                hunanXyear51 |   .0246082   .0277112     0.89   0.375    -.0299647    .0791812
                hunanXyear52 |   .0561049   .0287732     1.95   0.052    -.0005594    .1127693
                hunanXyear53 |   .1120623   .0427355     2.62   0.009     .0279012    .1962234
                hunanXyear54 |   .1333318   .0545891     2.44   0.015     .0258268    .2408367
                hunanXyear55 |   .0545401     .05238     1.04   0.299    -.0486144    .1576945
                hunanXyear56 |   .0596106   .0415533     1.43   0.153    -.0222223    .1414436
                hunanXyear57 |   .0542165    .042142     1.29   0.199    -.0287758    .1372087
                hunanXyear58 |   .0480932   .0359342     1.34   0.182    -.0226737    .1188601
                hunanXyear59 |   .0336603    .038956     0.86   0.388    -.0430576    .1103781
                hunanXyear60 |   .0323933   .0417129     0.78   0.438    -.0497539    .1145405
                hunanXyear61 |     .05689   .0469327     1.21   0.227    -.0355369    .1493168
                hunanXyear62 |   .0760714   .0740197     1.03   0.305     -.069699    .2218419
                hunanXyear63 |   .1505389   .1076107     1.40   0.163     -.061384    .3624618
                hunanXyear64 |    .121199   .1119583     1.08   0.280    -.0992857    .3416838
                hunanXyear65 |   .1082765   .0929491     1.16   0.245    -.0747727    .2913256
                hunanXyear66 |   .1560313   .1012712     1.54   0.125    -.0434069    .3554695
                hunanXyear67 |    .130565   .1263433     1.03   0.302    -.1182488    .3793788
                hunanXyear68 |   .1395268   .1265945     1.10   0.271    -.1097818    .3888354
                hunanXyear69 |   .1195324   .1164601     1.03   0.306    -.1098181    .3488829
                hunanXyear70 |   .0970973   .1081214     0.90   0.370    -.1158314    .3100259
                hunanXyear71 |   .0654487     .08174     0.80   0.424    -.0955258    .2264232
                hunanXyear72 |   .1017517   .0790542     1.29   0.199    -.0539336     .257437
                hunanXyear73 |   .0961603   .0730283     1.32   0.189    -.0476578    .2399783
                hunanXyear74 |   .0668051    .074036     0.90   0.368    -.0789974    .2126076
                hunanXyear75 |   .1018359   .0816653     1.25   0.214    -.0589914    .2626631
                hunanXyear76 |    .176999   .1292163     1.37   0.172    -.0774728    .4314707
                hunanXyear77 |   .1156924   .0901503     1.28   0.201    -.0618448    .2932297
                hunanXyear78 |   .1233242   .0826108     1.49   0.137    -.0393652    .2860135
                hunanXyear79 |    .127765   .1064046     1.20   0.231    -.0817827    .3373126
                hunanXyear80 |   .1835181   .1326931     1.38   0.168    -.0778008    .4448369
                hunanXyear81 |   .1771439   .1092961     1.62   0.106    -.0380981    .3923859
                hunanXyear82 |   .2041256   .1253318     1.63   0.105    -.0426964    .4509475
                hunanXyear83 |   .1145663   .0826071     1.39   0.167    -.0481158    .2772484
                hunanXyear84 |   .1071195   .0927143     1.16   0.249    -.0754673    .2897062
                hunanXyear85 |   .1101542    .113253     0.97   0.332    -.1128802    .3331887
                hunanXyear86 |   .1491812    .091527     1.63   0.104    -.0310672    .3294296
                hunanXyear87 |   .1416086   .0731096     1.94   0.054    -.0023697    .2855869
                hunanXyear88 |   .1393769   .0805402     1.73   0.085    -.0192348    .2979885
                hunanXyear89 |   .1571476   .1056162     1.49   0.138    -.0508473    .3651426
                hunanXyear90 |   .1436385    .089113     1.61   0.108    -.0318559     .319133
                hunanXyear91 |   .1277545   .0808613     1.58   0.115    -.0314894    .2869985
                hunanXyear92 |    .155477    .097508     1.59   0.112    -.0365501    .3475042
                hunanXyear93 |   .1412871   .0939513     1.50   0.134    -.0437356    .3263099
                hunanXyear94 |   .1615561   .0951405     1.70   0.091    -.0258087    .3489208
                hunanXyear95 |   .1245234   .0979537     1.27   0.205    -.0683814    .3174283
                hunanXyear96 |   .1374541   .1062293     1.29   0.197    -.0717484    .3466565
                hunanXyear97 |   .0873778   .0728119     1.20   0.231    -.0560141    .2307698
                hunanXyear98 |   .1213915   .0820922     1.48   0.140    -.0402765    .2830595
                hunanXyear99 |   .1458734   .0829506     1.76   0.080    -.0174852     .309232
               hunanXyear100 |   .1116928   .0669185     1.67   0.096     -.020093    .2434786
               hunanXyear101 |   .1300761   .0666897     1.95   0.052    -.0012591    .2614113
               hunanXyear102 |   .0901239   .0664329     1.36   0.176    -.0407055    .2209533
               hunanXyear103 |   .1053994   .0755919     1.39   0.164    -.0434672    .2542661
               hunanXyear104 |   .0581398   .0766006     0.76   0.449    -.0927134    .2089931
               hunanXyear105 |   .0652159   .0710546     0.92   0.360    -.0747153    .2051471
               hunanXyear106 |    .039675   .0686009     0.58   0.564    -.0954241    .1747741
               hunanXyear107 |  -.0022831    .072683    -0.03   0.975    -.1454212     .140855
               hunanXyear108 |   .0270807   .0860872     0.31   0.753    -.1424548    .1966163
               hunanXyear109 |   .0621328    .116658     0.53   0.595    -.1676074     .291873
               hunanXyear110 |   .0448056   .0875517     0.51   0.609    -.1276141    .2172254
               hunanXyear111 |   .0217295   .0777558     0.28   0.780    -.1313987    .1748578
           lnurbanpopXperiod |   .0092927   .0037415     2.48   0.014     .0019244    .0166611
             prefcapXperiod1 |   -.019208   .0333471    -0.58   0.565    -.0848801     .046464
             prefcapXperiod2 |  -.0202483   .0445217    -0.45   0.650     -.107927    .0674305
              prefcapXperiod |   .0146069   .0280084     0.52   0.602    -.0405512    .0697651
            lnjinshiXperiod1 |  -.0004222   .0149765    -0.03   0.978    -.0299161    .0290717
            lnjinshiXperiod2 |   .0184649   .0181033     1.02   0.309    -.0171868    .0541167
             lnjinshiXperiod |  -.0306182   .0132505    -2.31   0.022    -.0567131   -.0045233
        lncntyquota0Xperiod1 |  -.0438011    .020601    -2.13   0.034    -.0843718   -.0032305
        lncntyquota0Xperiod2 |  -.0585219   .0270121    -2.17   0.031    -.1117181   -.0053257
         lncntyquota0Xperiod |   .0320996   .0148644     2.16   0.032     .0028265    .0613728
           lncntypopXperiod1 |   .0368666   .0173789     2.12   0.035     .0026415    .0710917
           lncntypopXperiod2 |   .0519869   .0238498     2.18   0.030     .0050183    .0989555
            lncntypopXperiod |  -.0154882   .0115652    -1.34   0.182     -.038264    .0072877
          lncntyareaXperiod1 |  -.0065753    .015686    -0.42   0.675    -.0374665     .024316
          lncntyareaXperiod2 |    .008186   .0190982     0.43   0.669     -.029425    .0457971
           lncntyareaXperiod |  -.0164853   .0103176    -1.60   0.111    -.0368043    .0038337
             mainrivXperiod1 |   .0128739   .0184831     0.70   0.487    -.0235258    .0492736
             mainrivXperiod2 |     .01936   .0250268     0.77   0.440    -.0299265    .0686465
              mainrivXperiod |  -.0219817   .0154967    -1.42   0.157    -.0525001    .0085368
          dist2canalXperiod1 |   -.016694   .0055895    -2.99   0.003    -.0277017   -.0056862
          dist2canalXperiod2 |  -.0068561   .0087229    -0.79   0.433    -.0240345    .0103222
           dist2canalXperiod |   .0119741   .0054387     2.20   0.029     .0012634    .0226849
              lnriceXperiod1 |   .0639345   .0307253     2.08   0.038     .0034257    .1244433
              lnriceXperiod2 |   .0128997   .0532422     0.24   0.809    -.0919527    .1177521
               lnriceXperiod |  -.0420103   .0281529    -1.49   0.137    -.0974531    .0134325
             lnwheatXperiod1 |   .0777612   .0267476     2.91   0.004      .025086    .1304364
             lnwheatXperiod2 |   .0224389   .0356201     0.63   0.529    -.0477096    .0925873
              lnwheatXperiod |  -.0683396   .0261807    -2.61   0.010    -.1198985   -.0167808
        dist_nanjingXperiod1 |   .0223693    .006912     3.24   0.001     .0087571    .0359815
        dist_nanjingXperiod2 |   .0100404    .012264     0.82   0.414    -.0141118    .0341925
         dist_nanjingXperiod |  -.0142244    .006681    -2.13   0.034    -.0273816   -.0010673
      Taiping_route1Xperiod1 |  -.0782821   .0429655    -1.82   0.070    -.1628962    .0063319
      Taiping_route1Xperiod2 |  -.0938151   .0586924    -1.60   0.111    -.2094009    .0217706
       Taiping_route1Xperiod |   .0113478   .0637372     0.18   0.859    -.1141729    .1368685
                       _cons |  -.0464429   .1065671    -0.44   0.663    -.2563106    .1634248
----------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |       111           0         111     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. parmest, saving( Results\ConnectionOnSenior_all0_yearly_01, replace)
file Results\ConnectionOnSenior_all0_yearly_01.dta saved

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. reghdfe alloff  hXZeng_all0_invdistXyear22-hXZeng_all0_invdistXyear111  Zeng_all0_invdistXyear22-Zeng_all0_in
> vdistXyear111 hunanXyear22-hunanXyear111  lnurbanpopXperiod-Taiping_route1Xperiod   ,  absorb(year samcntyid 
> ) cluster(prefid )
(MWFE estimator converged in 2 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =    182,706
Absorbing 2 HDFE groups                           F( 304,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.3779
                                                  Adj R-squared   =     0.3708
                                                  Within R-sq.    =     0.0162
Number of clusters (prefid)  =        255         Root MSE        =     0.4270

                                              (Std. Err. adjusted for 255 clusters in prefid)
---------------------------------------------------------------------------------------------
                            |               Robust
                     alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
 hXZeng_all0_invdistXyear22 |   .0041888   .0099502     0.42   0.674    -.0154066    .0237842
 hXZeng_all0_invdistXyear23 |   .0321685   .0307831     1.05   0.297    -.0284541    .0927912
 hXZeng_all0_invdistXyear24 |   .0595485   .0325615     1.83   0.069    -.0045763    .1236734
 hXZeng_all0_invdistXyear25 |   .0662229    .034179     1.94   0.054    -.0010875    .1335333
 hXZeng_all0_invdistXyear26 |   .0584519   .0367265     1.59   0.113    -.0138754    .1307792
 hXZeng_all0_invdistXyear27 |   .0541656   .0359343     1.51   0.133    -.0166016    .1249328
 hXZeng_all0_invdistXyear28 |   .0573675    .035922     1.60   0.112    -.0133755    .1281104
 hXZeng_all0_invdistXyear29 |   .0110296   .0134003     0.82   0.411    -.0153602    .0374194
 hXZeng_all0_invdistXyear30 |   .0432819   .0243133     1.78   0.076    -.0045995    .0911632
 hXZeng_all0_invdistXyear31 |   .0372057   .0207507     1.79   0.074    -.0036597    .0780711
 hXZeng_all0_invdistXyear32 |   .0295481   .0423474     0.70   0.486    -.0538486    .1129449
 hXZeng_all0_invdistXyear33 |   .0387911   .0519612     0.75   0.456    -.0635385    .1411208
 hXZeng_all0_invdistXyear34 |   .0439343   .0456044     0.96   0.336    -.0458767    .1337453
 hXZeng_all0_invdistXyear35 |   .0158678   .0349207     0.45   0.650    -.0529033    .0846389
 hXZeng_all0_invdistXyear36 |   .0316812   .0371351     0.85   0.394    -.0414508    .1048132
 hXZeng_all0_invdistXyear37 |   .0254848   .0357499     0.71   0.477    -.0449192    .0958889
 hXZeng_all0_invdistXyear38 |   .0285692    .042212     0.68   0.499     -.054561    .1116994
 hXZeng_all0_invdistXyear39 |   .0358797   .0397708     0.90   0.368    -.0424427    .1142022
 hXZeng_all0_invdistXyear40 |    .015471   .0418641     0.37   0.712    -.0669741     .097916
 hXZeng_all0_invdistXyear41 |   .0217501   .0482876     0.45   0.653    -.0733451    .1168452
 hXZeng_all0_invdistXyear42 |   .0603301   .0453206     1.33   0.184    -.0289218     .149582
 hXZeng_all0_invdistXyear43 |   .0457608    .040591     1.13   0.261    -.0341769    .1256986
 hXZeng_all0_invdistXyear44 |   .0257358    .034305     0.75   0.454    -.0418226    .0932943
 hXZeng_all0_invdistXyear45 |   .0271336   .0404398     0.67   0.503    -.0525064    .1067736
 hXZeng_all0_invdistXyear46 |   .0371442   .0513031     0.72   0.470    -.0638894    .1381777
 hXZeng_all0_invdistXyear47 |    .037477   .0509623     0.74   0.463    -.0628855    .1378394
 hXZeng_all0_invdistXyear48 |    .056652    .039998     1.42   0.158    -.0221179    .1354219
 hXZeng_all0_invdistXyear49 |   .0453182   .0386683     1.17   0.242    -.0308331    .1214695
 hXZeng_all0_invdistXyear50 |   .0401177   .0268457     1.49   0.136    -.0127509    .0929862
 hXZeng_all0_invdistXyear51 |   .0486256   .0281138     1.73   0.085    -.0067402    .1039914
 hXZeng_all0_invdistXyear52 |   .0186317   .0163922     1.14   0.257    -.0136503    .0509136
 hXZeng_all0_invdistXyear53 |   .0215977   .0248862     0.87   0.386    -.0274119    .0706074
 hXZeng_all0_invdistXyear54 |   .0098562   .0251147     0.39   0.695    -.0396034    .0593158
 hXZeng_all0_invdistXyear55 |   .0513577   .0358837     1.43   0.154    -.0193097    .1220252
 hXZeng_all0_invdistXyear56 |   .0355252   .0129034     2.75   0.006     .0101139    .0609364
 hXZeng_all0_invdistXyear57 |    .040351   .0291287     1.39   0.167    -.0170135    .0977154
 hXZeng_all0_invdistXyear58 |    .059171   .0273121     2.17   0.031     .0053839    .1129581
 hXZeng_all0_invdistXyear59 |   .0232626   .0168263     1.38   0.168    -.0098742    .0563993
 hXZeng_all0_invdistXyear60 |   .0489624   .0244763     2.00   0.047     .0007601    .0971648
 hXZeng_all0_invdistXyear61 |   .0272505   .0188143     1.45   0.149    -.0098014    .0643025
 hXZeng_all0_invdistXyear62 |    .132181   .0180703     7.31   0.000     .0965943    .1677676
 hXZeng_all0_invdistXyear63 |   .1339034   .0250869     5.34   0.000     .0844985    .1833083
 hXZeng_all0_invdistXyear64 |   .1759526   .0222228     7.92   0.000     .1321881     .219717
 hXZeng_all0_invdistXyear65 |   .1684922   .0324903     5.19   0.000     .1045075     .232477
 hXZeng_all0_invdistXyear66 |   .1543059   .0296561     5.20   0.000     .0959027    .2127091
 hXZeng_all0_invdistXyear67 |    .222601   .0426045     5.22   0.000     .1386979    .3065041
 hXZeng_all0_invdistXyear68 |   .1351392   .0255001     5.30   0.000     .0849207    .1853577
 hXZeng_all0_invdistXyear69 |   .1394666   .0163525     8.53   0.000     .1072627    .1716704
 hXZeng_all0_invdistXyear70 |   .1242036   .0199359     6.23   0.000      .084943    .1634642
 hXZeng_all0_invdistXyear71 |   .0907951   .0217199     4.18   0.000     .0480211    .1335691
 hXZeng_all0_invdistXyear72 |    .055288   .0157618     3.51   0.001     .0242476    .0863284
 hXZeng_all0_invdistXyear73 |   .0738601   .0136774     5.40   0.000     .0469246    .1007957
 hXZeng_all0_invdistXyear74 |    .070809   .0188833     3.75   0.000     .0336212    .1079969
 hXZeng_all0_invdistXyear75 |   .0755399   .0142219     5.31   0.000      .047532    .1035478
 hXZeng_all0_invdistXyear76 |   .0846482   .0187406     4.52   0.000     .0477413     .121555
 hXZeng_all0_invdistXyear77 |   .0559421   .0170961     3.27   0.001     .0222739    .0896102
 hXZeng_all0_invdistXyear78 |   .0698861   .0117569     5.94   0.000     .0467327    .0930394
 hXZeng_all0_invdistXyear79 |   .1158249   .0150118     7.72   0.000     .0862614    .1453883
 hXZeng_all0_invdistXyear80 |   .0556971   .0279099     2.00   0.047     .0007329    .1106614
 hXZeng_all0_invdistXyear81 |   .0902252   .0192418     4.69   0.000     .0523314    .1281191
 hXZeng_all0_invdistXyear82 |   .1321451   .0183535     7.20   0.000     .0960007    .1682895
 hXZeng_all0_invdistXyear83 |   .1277342   .0459104     2.78   0.006     .0373207    .2181478
 hXZeng_all0_invdistXyear84 |   .1643093   .0428406     3.84   0.000     .0799413    .2486773
 hXZeng_all0_invdistXyear85 |   .1882343   .0494012     3.81   0.000     .0909461    .2855225
 hXZeng_all0_invdistXyear86 |   .1325788   .0555923     2.38   0.018     .0230983    .2420594
 hXZeng_all0_invdistXyear87 |   .1082739   .0490444     2.21   0.028     .0116885    .2048593
 hXZeng_all0_invdistXyear88 |   .1552122   .0505502     3.07   0.002     .0556613     .254763
 hXZeng_all0_invdistXyear89 |    .136386   .0529304     2.58   0.011     .0321475    .2406244
 hXZeng_all0_invdistXyear90 |   .0987892   .0485154     2.04   0.043     .0032455    .1943328
 hXZeng_all0_invdistXyear91 |   .1125058    .057522     1.96   0.052     -.000775    .2257865
 hXZeng_all0_invdistXyear92 |   .0444169   .0545615     0.81   0.416    -.0630337    .1518675
 hXZeng_all0_invdistXyear93 |   .0762351   .0551948     1.38   0.168    -.0324627    .1849329
 hXZeng_all0_invdistXyear94 |   .0736923   .0493879     1.49   0.137    -.0235697    .1709544
 hXZeng_all0_invdistXyear95 |   .0716346   .0536837     1.33   0.183    -.0340873    .1773565
 hXZeng_all0_invdistXyear96 |   .1092445   .0576443     1.90   0.059    -.0042772    .2227661
 hXZeng_all0_invdistXyear97 |    .089354   .0511827     1.75   0.082    -.0114426    .1901505
 hXZeng_all0_invdistXyear98 |    .039904   .0447799     0.89   0.374    -.0482832    .1280913
 hXZeng_all0_invdistXyear99 |   .0081648   .0257627     0.32   0.752    -.0425709    .0589004
hXZeng_all0_invdistXyear100 |   .0490592     .02998     1.64   0.103    -.0099819    .1081002
hXZeng_all0_invdistXyear101 |   .0082442   .0311797     0.26   0.792    -.0531595     .069648
hXZeng_all0_invdistXyear102 |   .0155444   .0246647     0.63   0.529    -.0330289    .0641178
hXZeng_all0_invdistXyear103 |   .0610192   .0379916     1.61   0.109    -.0137995    .1358379
hXZeng_all0_invdistXyear104 |   .0538704   .0517488     1.04   0.299     -.048041    .1557819
hXZeng_all0_invdistXyear105 |   .0713275   .0433827     1.64   0.101    -.0141082    .1567632
hXZeng_all0_invdistXyear106 |   .0469736   .0426622     1.10   0.272     -.037043    .1309902
hXZeng_all0_invdistXyear107 |   .0216722   .0553645     0.39   0.696    -.0873599    .1307042
hXZeng_all0_invdistXyear108 |   .0074041   .0354688     0.21   0.835    -.0624462    .0772545
hXZeng_all0_invdistXyear109 |    .041212    .044657     0.92   0.357     -.046733    .1291571
hXZeng_all0_invdistXyear110 |   .0331505   .0456366     0.73   0.468    -.0567238    .1230247
hXZeng_all0_invdistXyear111 |   .0130883   .0461834     0.28   0.777    -.0778629    .1040394
   Zeng_all0_invdistXyear22 |   .0118406   .0082216     1.44   0.151    -.0043505    .0280318
   Zeng_all0_invdistXyear23 |  -.0136896   .0241413    -0.57   0.571    -.0612322    .0338531
   Zeng_all0_invdistXyear24 |  -.0436531   .0300506    -1.45   0.148    -.1028332     .015527
   Zeng_all0_invdistXyear25 |  -.0557394   .0334014    -1.67   0.096    -.1215185    .0100396
   Zeng_all0_invdistXyear26 |  -.0476218   .0355905    -1.34   0.182    -.1177118    .0224683
   Zeng_all0_invdistXyear27 |  -.0513079    .034914    -1.47   0.143    -.1200656    .0174498
   Zeng_all0_invdistXyear28 |  -.0502162   .0345924    -1.45   0.148    -.1183407    .0179082
   Zeng_all0_invdistXyear29 |   .0020928   .0083107     0.25   0.801    -.0142739    .0184596
   Zeng_all0_invdistXyear30 |  -.0400221   .0241103    -1.66   0.098    -.0875036    .0074594
   Zeng_all0_invdistXyear31 |  -.0286679   .0201697    -1.42   0.156    -.0683892    .0110533
   Zeng_all0_invdistXyear32 |  -.0263438   .0418449    -0.63   0.530     -.108751    .0560635
   Zeng_all0_invdistXyear33 |  -.0362246   .0519348    -0.70   0.486    -.1385022     .066053
   Zeng_all0_invdistXyear34 |  -.0386502   .0452732    -0.85   0.394    -.1278089    .0505085
   Zeng_all0_invdistXyear35 |  -.0067709   .0343096    -0.20   0.844    -.0743384    .0607967
   Zeng_all0_invdistXyear36 |  -.0201578   .0369223    -0.55   0.586    -.0928706    .0525549
   Zeng_all0_invdistXyear37 |  -.0245174    .035693    -0.69   0.493    -.0948092    .0457745
   Zeng_all0_invdistXyear38 |  -.0276017   .0421638    -0.65   0.513    -.1106369    .0554334
   Zeng_all0_invdistXyear39 |  -.0349123   .0397195    -0.88   0.380    -.1131338    .0433093
   Zeng_all0_invdistXyear40 |  -.0101869   .0415031    -0.25   0.806    -.0919209    .0715471
   Zeng_all0_invdistXyear41 |  -.0134249   .0482577    -0.28   0.781    -.1084611    .0816113
   Zeng_all0_invdistXyear42 |   -.041047   .0452773    -0.91   0.365    -.1302136    .0481197
   Zeng_all0_invdistXyear43 |  -.0359151   .0405131    -0.89   0.376    -.1156995    .0438693
   Zeng_all0_invdistXyear44 |  -.0063187   .0342304    -0.18   0.854    -.0737302    .0610928
   Zeng_all0_invdistXyear45 |  -.0144364    .040266    -0.36   0.720    -.0937341    .0648614
   Zeng_all0_invdistXyear46 |  -.0250061   .0512371    -0.49   0.626    -.1259099    .0758976
   Zeng_all0_invdistXyear47 |  -.0246457   .0509057    -0.48   0.629    -.1248967    .0756054
   Zeng_all0_invdistXyear48 |  -.0205183   .0399389    -0.51   0.608    -.0991718    .0581352
   Zeng_all0_invdistXyear49 |  -.0236087    .038305    -0.62   0.538    -.0990445    .0518271
   Zeng_all0_invdistXyear50 |   .0048388   .0232437     0.21   0.835    -.0409361    .0506137
   Zeng_all0_invdistXyear51 |  -.0299773    .025873    -1.16   0.248    -.0809302    .0209756
   Zeng_all0_invdistXyear52 |  -.0053168   .0144717    -0.37   0.714    -.0338165     .023183
   Zeng_all0_invdistXyear53 |   .0264813   .0178775     1.48   0.140    -.0087257    .0616883
   Zeng_all0_invdistXyear54 |  -.0103832   .0210778    -0.49   0.623    -.0518926    .0311263
   Zeng_all0_invdistXyear55 |  -.0242928   .0343552    -0.71   0.480    -.0919501    .0433645
   Zeng_all0_invdistXyear56 |  -.0114458   .0111037    -1.03   0.304    -.0333128    .0104212
   Zeng_all0_invdistXyear57 |  -.0254964   .0277388    -0.92   0.359    -.0801238     .029131
   Zeng_all0_invdistXyear58 |  -.0318379   .0254379    -1.25   0.212    -.0819341    .0182582
   Zeng_all0_invdistXyear59 |  -.0074467   .0130766    -0.57   0.570    -.0331991    .0183056
   Zeng_all0_invdistXyear60 |  -.0237092   .0216197    -1.10   0.274    -.0662859    .0188676
   Zeng_all0_invdistXyear61 |  -.0045577   .0136689    -0.33   0.739    -.0314765    .0223611
   Zeng_all0_invdistXyear62 |  -.0161998    .015301    -1.06   0.291    -.0463328    .0139331
   Zeng_all0_invdistXyear63 |   .0115085   .0134421     0.86   0.393    -.0149637    .0379806
   Zeng_all0_invdistXyear64 |   .0177187   .0112241     1.58   0.116    -.0043855     .039823
   Zeng_all0_invdistXyear65 |  -.0087347   .0265724    -0.33   0.743    -.0610651    .0435957
   Zeng_all0_invdistXyear66 |  -.0194331   .0243429    -0.80   0.425    -.0673728    .0285066
   Zeng_all0_invdistXyear67 |  -.0373105   .0391741    -0.95   0.342    -.1144579     .039837
   Zeng_all0_invdistXyear68 |   .0090925   .0204424     0.44   0.657    -.0311657    .0493507
   Zeng_all0_invdistXyear69 |   .0213245   .0128667     1.66   0.099    -.0040145    .0466635
   Zeng_all0_invdistXyear70 |  -.0060705   .0162175    -0.37   0.708    -.0380084    .0258674
   Zeng_all0_invdistXyear71 |   .0142218   .0193059     0.74   0.462    -.0237982    .0522418
   Zeng_all0_invdistXyear72 |   .0073621   .0093787     0.78   0.433    -.0111078    .0258319
   Zeng_all0_invdistXyear73 |  -.0070506   .0100752    -0.70   0.485    -.0268922     .012791
   Zeng_all0_invdistXyear74 |  -.0189829   .0170601    -1.11   0.267    -.0525802    .0146144
   Zeng_all0_invdistXyear75 |  -.0160095   .0078163    -2.05   0.042    -.0314025   -.0006165
   Zeng_all0_invdistXyear76 |   .0090317   .0139894     0.65   0.519    -.0185182    .0365816
   Zeng_all0_invdistXyear77 |   .0201709   .0149398     1.35   0.178    -.0092509    .0495926
   Zeng_all0_invdistXyear78 |  -.0005716   .0084768    -0.07   0.946    -.0172654    .0161222
   Zeng_all0_invdistXyear79 |   .0048132    .012109     0.40   0.691    -.0190336      .02866
   Zeng_all0_invdistXyear80 |   .0307037   .0257212     1.19   0.234    -.0199502    .0813576
   Zeng_all0_invdistXyear81 |   .0035887   .0162318     0.22   0.825    -.0283773    .0355547
   Zeng_all0_invdistXyear82 |  -.0039136   .0161653    -0.24   0.809    -.0357487    .0279215
   Zeng_all0_invdistXyear83 |  -.0262946   .0451851    -0.58   0.561    -.1152796    .0626905
   Zeng_all0_invdistXyear84 |  -.0319415   .0420497    -0.76   0.448    -.1147518    .0508689
   Zeng_all0_invdistXyear85 |  -.0342972   .0489492    -0.70   0.484    -.1306952    .0621008
   Zeng_all0_invdistXyear86 |  -.0054658   .0543312    -0.10   0.920    -.1124628    .1015311
   Zeng_all0_invdistXyear87 |  -.0151533   .0479269    -0.32   0.752    -.1095379    .0792314
   Zeng_all0_invdistXyear88 |  -.0353228   .0492848    -0.72   0.474    -.1323818    .0617361
   Zeng_all0_invdistXyear89 |  -.0195377   .0474351    -0.41   0.681     -.112954    .0738786
   Zeng_all0_invdistXyear90 |  -.0040694   .0470437    -0.09   0.931    -.0967149    .0885761
   Zeng_all0_invdistXyear91 |  -.0199443    .056649    -0.35   0.725    -.1315059    .0916173
   Zeng_all0_invdistXyear92 |  -.0086464    .054237    -0.16   0.873     -.115458    .0981651
   Zeng_all0_invdistXyear93 |  -.0406772    .054863    -0.74   0.459    -.1487216    .0673671
   Zeng_all0_invdistXyear94 |  -.0363228   .0482815    -0.75   0.453    -.1314059    .0587603
   Zeng_all0_invdistXyear95 |  -.0251188   .0518314    -0.48   0.628    -.1271928    .0769552
   Zeng_all0_invdistXyear96 |  -.0385204   .0554874    -0.69   0.488    -.1477943    .0707536
   Zeng_all0_invdistXyear97 |    -.05326   .0494527    -1.08   0.283    -.1506495    .0441295
   Zeng_all0_invdistXyear98 |  -.0212753   .0394386    -0.54   0.590    -.0989436     .056393
   Zeng_all0_invdistXyear99 |   .0038781   .0215362     0.18   0.857    -.0385342    .0462903
  Zeng_all0_invdistXyear100 |  -.0315489   .0215457    -1.46   0.144    -.0739799    .0108821
  Zeng_all0_invdistXyear101 |   .0024121   .0263128     0.09   0.927    -.0494069    .0542312
  Zeng_all0_invdistXyear102 |   .0054321   .0198573     0.27   0.785    -.0336738    .0445379
  Zeng_all0_invdistXyear103 |  -.0330316   .0367889    -0.90   0.370    -.1054818    .0394186
  Zeng_all0_invdistXyear104 |  -.0339338   .0506304    -0.67   0.503    -.1336426     .065775
  Zeng_all0_invdistXyear105 |  -.0556289   .0428102    -1.30   0.195    -.1399372    .0286793
  Zeng_all0_invdistXyear106 |  -.0355131   .0421342    -0.84   0.400    -.1184899    .0474637
  Zeng_all0_invdistXyear107 |  -.0060522   .0553207    -0.11   0.913    -.1149979    .1028936
  Zeng_all0_invdistXyear108 |   .0174407   .0349057     0.50   0.618    -.0513007    .0861822
  Zeng_all0_invdistXyear109 |  -.0096242   .0441321    -0.22   0.828    -.0965357    .0772873
  Zeng_all0_invdistXyear110 |  -.0129457   .0452705    -0.29   0.775    -.1020991    .0762076
  Zeng_all0_invdistXyear111 |   .0080777   .0459548     0.18   0.861    -.0824233    .0985787
               hunanXyear22 |   .0294538   .0306573     0.96   0.338    -.0309211    .0898287
               hunanXyear23 |   .0631183   .0546088     1.16   0.249    -.0444253    .1706619
               hunanXyear24 |   .0271968   .0393119     0.69   0.490    -.0502219    .1046155
               hunanXyear25 |   .0177507   .0402323     0.44   0.659    -.0614807    .0969822
               hunanXyear26 |   .0086045    .031583     0.27   0.786    -.0535933    .0708023
               hunanXyear27 |   .0306602   .0420757     0.73   0.467    -.0522015    .1135219
               hunanXyear28 |   .0152978   .0372209     0.41   0.681     -.058003    .0885987
               hunanXyear29 |   .0584801   .0277805     2.11   0.036     .0037707    .1131895
               hunanXyear30 |   .0045138   .0209946     0.21   0.830    -.0368318    .0458594
               hunanXyear31 |   .0218893   .0279293     0.78   0.434    -.0331133    .0768918
               hunanXyear32 |   .0197497   .0637385     0.31   0.757    -.1057736     .145273
               hunanXyear33 |  -.0348087   .0370293    -0.94   0.348    -.1077323    .0381148
               hunanXyear34 |   .0155436   .0541789     0.29   0.774    -.0911534    .1222406
               hunanXyear35 |   .0296922   .0399731     0.74   0.458    -.0490287    .1084132
               hunanXyear36 |   .0122618   .0427818     0.29   0.775    -.0719904    .0965141
               hunanXyear37 |   -.022639   .0245607    -0.92   0.358    -.0710076    .0257297
               hunanXyear38 |  -.0329218   .0274003    -1.20   0.231    -.0868825    .0210389
               hunanXyear39 |  -.0306788   .0263374    -1.16   0.245    -.0825462    .0211886
               hunanXyear40 |   .0194319   .0513128     0.38   0.705    -.0816209    .1204846
               hunanXyear41 |  -.0232711   .0329725    -0.71   0.481    -.0882055    .0416632
               hunanXyear42 |  -.0153112   .0436328    -0.35   0.726    -.1012394    .0706169
               hunanXyear43 |  -.0200168   .0279334    -0.72   0.474    -.0750274    .0349937
               hunanXyear44 |  -.0284815   .0250032    -1.14   0.256    -.0777215    .0207586
               hunanXyear45 |   .0018443   .0305676     0.06   0.952     -.058354    .0620426
               hunanXyear46 |  -.0087696   .0346436    -0.25   0.800     -.076995    .0594557
               hunanXyear47 |  -.0189365   .0338494    -0.56   0.576    -.0855977    .0477247
               hunanXyear48 |  -.0087229   .0339959    -0.26   0.798    -.0756726    .0582269
               hunanXyear49 |  -.0063101   .0309815    -0.20   0.839    -.0673234    .0547031
               hunanXyear50 |   .0260669   .0366128     0.71   0.477    -.0460364    .0981702
               hunanXyear51 |   .0246082   .0277112     0.89   0.375    -.0299647    .0791812
               hunanXyear52 |   .0561049   .0287732     1.95   0.052    -.0005594    .1127693
               hunanXyear53 |   .1120623   .0427355     2.62   0.009     .0279012    .1962234
               hunanXyear54 |   .1333318   .0545891     2.44   0.015     .0258268    .2408367
               hunanXyear55 |   .0545401     .05238     1.04   0.299    -.0486144    .1576945
               hunanXyear56 |   .0596106   .0415533     1.43   0.153    -.0222223    .1414436
               hunanXyear57 |   .0542165    .042142     1.29   0.199    -.0287758    .1372087
               hunanXyear58 |   .0480932   .0359342     1.34   0.182    -.0226737    .1188601
               hunanXyear59 |   .0336603    .038956     0.86   0.388    -.0430576    .1103781
               hunanXyear60 |   .0323933   .0417129     0.78   0.438    -.0497539    .1145405
               hunanXyear61 |     .05689   .0469327     1.21   0.227    -.0355369    .1493168
               hunanXyear62 |   .0760714   .0740197     1.03   0.305     -.069699    .2218419
               hunanXyear63 |   .1505389   .1076107     1.40   0.163     -.061384    .3624618
               hunanXyear64 |    .121199   .1119583     1.08   0.280    -.0992857    .3416838
               hunanXyear65 |   .1082765   .0929491     1.16   0.245    -.0747727    .2913256
               hunanXyear66 |   .1560313   .1012712     1.54   0.125    -.0434069    .3554695
               hunanXyear67 |    .130565   .1263433     1.03   0.302    -.1182488    .3793788
               hunanXyear68 |   .1395268   .1265945     1.10   0.271    -.1097818    .3888354
               hunanXyear69 |   .1195324   .1164601     1.03   0.306    -.1098181    .3488829
               hunanXyear70 |   .0970973   .1081214     0.90   0.370    -.1158314    .3100259
               hunanXyear71 |   .0654487     .08174     0.80   0.424    -.0955258    .2264232
               hunanXyear72 |   .1017517   .0790542     1.29   0.199    -.0539336     .257437
               hunanXyear73 |   .0961603   .0730283     1.32   0.189    -.0476578    .2399783
               hunanXyear74 |   .0668051    .074036     0.90   0.368    -.0789974    .2126076
               hunanXyear75 |   .1018359   .0816653     1.25   0.214    -.0589914    .2626631
               hunanXyear76 |    .176999   .1292163     1.37   0.172    -.0774728    .4314707
               hunanXyear77 |   .1156924   .0901503     1.28   0.201    -.0618448    .2932297
               hunanXyear78 |   .1233242   .0826108     1.49   0.137    -.0393652    .2860135
               hunanXyear79 |    .127765   .1064046     1.20   0.231    -.0817827    .3373126
               hunanXyear80 |   .1835181   .1326931     1.38   0.168    -.0778008    .4448369
               hunanXyear81 |   .1771439   .1092961     1.62   0.106    -.0380981    .3923859
               hunanXyear82 |   .2041256   .1253318     1.63   0.105    -.0426964    .4509475
               hunanXyear83 |   .1145663   .0826071     1.39   0.167    -.0481158    .2772484
               hunanXyear84 |   .1071195   .0927143     1.16   0.249    -.0754673    .2897062
               hunanXyear85 |   .1101542    .113253     0.97   0.332    -.1128802    .3331887
               hunanXyear86 |   .1491812    .091527     1.63   0.104    -.0310672    .3294296
               hunanXyear87 |   .1416086   .0731096     1.94   0.054    -.0023697    .2855869
               hunanXyear88 |   .1393769   .0805402     1.73   0.085    -.0192348    .2979885
               hunanXyear89 |   .1571476   .1056162     1.49   0.138    -.0508473    .3651426
               hunanXyear90 |   .1436385    .089113     1.61   0.108    -.0318559     .319133
               hunanXyear91 |   .1277545   .0808613     1.58   0.115    -.0314894    .2869985
               hunanXyear92 |    .155477    .097508     1.59   0.112    -.0365501    .3475042
               hunanXyear93 |   .1412871   .0939513     1.50   0.134    -.0437356    .3263099
               hunanXyear94 |   .1615561   .0951405     1.70   0.091    -.0258087    .3489208
               hunanXyear95 |   .1245234   .0979537     1.27   0.205    -.0683814    .3174283
               hunanXyear96 |   .1374541   .1062293     1.29   0.197    -.0717484    .3466565
               hunanXyear97 |   .0873778   .0728119     1.20   0.231    -.0560141    .2307698
               hunanXyear98 |   .1213915   .0820922     1.48   0.140    -.0402765    .2830595
               hunanXyear99 |   .1458734   .0829506     1.76   0.080    -.0174852     .309232
              hunanXyear100 |   .1116928   .0669185     1.67   0.096     -.020093    .2434786
              hunanXyear101 |   .1300761   .0666897     1.95   0.052    -.0012591    .2614113
              hunanXyear102 |   .0901239   .0664329     1.36   0.176    -.0407055    .2209533
              hunanXyear103 |   .1053994   .0755919     1.39   0.164    -.0434672    .2542661
              hunanXyear104 |   .0581398   .0766006     0.76   0.449    -.0927134    .2089931
              hunanXyear105 |   .0652159   .0710546     0.92   0.360    -.0747153    .2051471
              hunanXyear106 |    .039675   .0686009     0.58   0.564    -.0954241    .1747741
              hunanXyear107 |  -.0022831    .072683    -0.03   0.975    -.1454212     .140855
              hunanXyear108 |   .0270807   .0860872     0.31   0.753    -.1424548    .1966163
              hunanXyear109 |   .0621328    .116658     0.53   0.595    -.1676074     .291873
              hunanXyear110 |   .0448056   .0875517     0.51   0.609    -.1276141    .2172254
              hunanXyear111 |   .0217295   .0777558     0.28   0.780    -.1313987    .1748578
          lnurbanpopXperiod |   .0092927   .0037415     2.48   0.014     .0019244    .0166611
            prefcapXperiod1 |   -.019208   .0333471    -0.58   0.565    -.0848801     .046464
            prefcapXperiod2 |  -.0202483   .0445217    -0.45   0.650     -.107927    .0674305
             prefcapXperiod |   .0146069   .0280084     0.52   0.602    -.0405512    .0697651
           lnjinshiXperiod1 |  -.0004222   .0149765    -0.03   0.978    -.0299161    .0290717
           lnjinshiXperiod2 |   .0184649   .0181033     1.02   0.309    -.0171868    .0541167
            lnjinshiXperiod |  -.0306182   .0132505    -2.31   0.022    -.0567131   -.0045233
       lncntyquota0Xperiod1 |  -.0438011    .020601    -2.13   0.034    -.0843718   -.0032305
       lncntyquota0Xperiod2 |  -.0585219   .0270121    -2.17   0.031    -.1117181   -.0053257
        lncntyquota0Xperiod |   .0320996   .0148644     2.16   0.032     .0028265    .0613728
          lncntypopXperiod1 |   .0368666   .0173789     2.12   0.035     .0026415    .0710917
          lncntypopXperiod2 |   .0519869   .0238498     2.18   0.030     .0050183    .0989555
           lncntypopXperiod |  -.0154882   .0115652    -1.34   0.182     -.038264    .0072877
         lncntyareaXperiod1 |  -.0065753    .015686    -0.42   0.675    -.0374665     .024316
         lncntyareaXperiod2 |    .008186   .0190982     0.43   0.669     -.029425    .0457971
          lncntyareaXperiod |  -.0164853   .0103176    -1.60   0.111    -.0368043    .0038337
            mainrivXperiod1 |   .0128739   .0184831     0.70   0.487    -.0235258    .0492736
            mainrivXperiod2 |     .01936   .0250268     0.77   0.440    -.0299265    .0686465
             mainrivXperiod |  -.0219817   .0154967    -1.42   0.157    -.0525001    .0085368
         dist2canalXperiod1 |   -.016694   .0055895    -2.99   0.003    -.0277017   -.0056862
         dist2canalXperiod2 |  -.0068561   .0087229    -0.79   0.433    -.0240345    .0103222
          dist2canalXperiod |   .0119741   .0054387     2.20   0.029     .0012634    .0226849
             lnriceXperiod1 |   .0639345   .0307253     2.08   0.038     .0034257    .1244433
             lnriceXperiod2 |   .0128997   .0532422     0.24   0.809    -.0919527    .1177521
              lnriceXperiod |  -.0420103   .0281529    -1.49   0.137    -.0974531    .0134325
            lnwheatXperiod1 |   .0777612   .0267476     2.91   0.004      .025086    .1304364
            lnwheatXperiod2 |   .0224389   .0356201     0.63   0.529    -.0477096    .0925873
             lnwheatXperiod |  -.0683396   .0261807    -2.61   0.010    -.1198985   -.0167808
       dist_nanjingXperiod1 |   .0223693    .006912     3.24   0.001     .0087571    .0359815
       dist_nanjingXperiod2 |   .0100404    .012264     0.82   0.414    -.0141118    .0341925
        dist_nanjingXperiod |  -.0142244    .006681    -2.13   0.034    -.0273816   -.0010673
     Taiping_route1Xperiod1 |  -.0782821   .0429655    -1.82   0.070    -.1628962    .0063319
     Taiping_route1Xperiod2 |  -.0938151   .0586924    -1.60   0.111    -.2094009    .0217706
      Taiping_route1Xperiod |   .0113478   .0637372     0.18   0.859    -.1141729    .1368685
                      _cons |  -.0464429   .1065671    -0.44   0.663    -.2563106    .1634248
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |       111           0         111     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. parmest, saving( Results\ConnectionOnSenior_all0_yearly_02, replace)
file Results\ConnectionOnSenior_all0_yearly_02.dta saved

. 
. 
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnSenior_all0_yearly_01, clear

. 
. 
. gen i=_n

. keep if i<=91
(214 observations deleted)

. gen time=i

. 
. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

. 
.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30    .0114615    .0100511          0   .0449565

. gen bench=r(mean)

. 
.  
. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) ) (line zero time , lp(dash) lw(medthick) lc(g
> s0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("A. Hunan", size(medium) )  xlabel(1820(30)1910) ylabel(-0.2(0.2)0.4
> )   legend(order(1 "95% CI" 2 "Effects of connections" ) row(2) region(color(none)))  graphregion(color(white
> ) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCnty_hunan_conn_All0_ctrl_0.gph", replace) 
> xsize(3) ysize(3)
(file Results\AllCnty_hunan_conn_All0_ctrl_0.gph saved)

.  
. restore 

. 
. 
. 
. 
. 
. 
. 
. 
. 
. *********************************************************************************************************
. ****  the connection effects in non-Hunan 
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnSenior_all0_yearly_01, clear

. 
. 
. gen i=_n

. keep if i>=91&i<=181
(214 observations deleted)

. gen time=i-90

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

. 
.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30   -.0247477    .0177066  -.0557394   .0118406

. gen bench=r(mean)

. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) )   (line zero time , lp(dash) lw(medthick) lc
> (gs0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("B. Non-Hunan", size(medium) )  xlabel(1820(30)1910) ylabel(-0.2(0.2
> )0.4)   legend(order(1 "95% CI" 2 "Effects of connections"  ) row(2) region(color(none)))  graphregion(color(
> white) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCnty_nhunan_conn_All0_ctrl_0.gph", rep
> lace) xsize(3) ysize(3)
(file Results\AllCnty_nhunan_conn_All0_ctrl_0.gph saved)

.  
. restore 

. 
. 
. 
. 
. 
. 
. *********************************************************************************************************
. ****  the difference in connection effects, hunan vs. non-hunan 
. 
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnSenior_all0_yearly_02, clear

. 
. 
. gen i=_n

. keep if i<=91
(214 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

. 
.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30    .0362093    .0171097          0   .0662229

. gen bench=r(mean)

. 
.  
. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) ) (line zero time , lp(dash) lw(medthick) lc(g
> s0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("C. Difference between Hunan and non-Hunan", size(medium) )  xlabel(
> 1820(30)1910) ylabel(-0.2(0.2)0.4)   legend(order(1 "95% CI" 2 "Effects of connections * Hunan" ) row(2) regi
> on(color(none)))  graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCn
> ty_hXconn_All0_ctrl_0.gph", replace) xsize(3) ysize(3)
(file Results\AllCnty_hXconn_All0_ctrl_0.gph saved)

.  
. restore 

. 
. 
. 
. 
. 
. *********************************************************************************************************
. ************************************************** Yearly effect of connections in Hunan, non-hunan, and the 
> difference 
. ************************************************** 
. 
. graph combine   Results\AllCnty_hunan_conn_All0_ctrl_0.gph Results\AllCnty_nhunan_conn_All0_ctrl_0.gph  Resul
> ts\AllCnty_hXconn_All0_ctrl_0.gph  , row(1) xsize(9) ysize(4.5) graphregion(color(white) ifcolor(white) ilcol
> or(white) fcolor(white))

. 
. graph export Results\Figure_6.png, replace
(file Results\Figure_6.png written in PNG format)

. 
end of do-file

. 
. 
. ******** Figure C.1. Understanding the Fluctuation of the Power Impact
. 
. 
. do Programs\Appendix_Figure_C1.do

. *********************************************************************************************************
. **** Figure C.1. Yearly effects of connections : Entry vs. Re-entry 
. *********************************************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen nhXZenghu_all_invdist=nonhunan*Zenghu_all_invdist

. gen hXZenghu_all_invdist=hunan*Zenghu_all_invdist

. 
. gen nhXZeng_all0_invdist=nonhunan*Zeng_all0_invdist

. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. foreach x of varlist hunan  Zenghu_all_invdist   Zeng_all0_invdist Zeng_all0_invdist_pc  invdist0_L1 invdist0
> _F1    Zeng_exam0_invdist  Zeng_Extraexam_invdist   Zeng_BMF_invdist    Zeng_juren0_invdist  nhXZenghu_all_in
> vdist nhXZeng_all0_invdist  hXZenghu_all_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod1=`x'*period1
  3. gen `x'Xperiod2=`x'*period2
  4. gen `x'Xperiod=`x'*period
  5. 
. }

. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod1=`x'*period1
  3. gen `x'Xperiod2=`x'*period2
  4. gen `x'Xperiod=`x'*period
  5. 
. }

. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing  Taiping_route1 {
  2. gen h`x'Xperiod1=hunan*`x'*period1
  3. gen h`x'Xperiod2=hunan*`x'*period2
  4. gen h`x'Xperiod=hunan*`x'*period
  5. }

. 
. 
. 
. 
. ********************************************************************************
. ********** gen year dummy & interactions
. 
. 
. drop if year==.
(0 observations deleted)

. tab year, gen(year)

       Year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1800 |      1,646        0.90        0.90
       1801 |      1,646        0.90        1.80
       1802 |      1,646        0.90        2.70
       1803 |      1,646        0.90        3.60
       1804 |      1,646        0.90        4.50
       1805 |      1,646        0.90        5.41
       1806 |      1,646        0.90        6.31
       1807 |      1,646        0.90        7.21
       1808 |      1,646        0.90        8.11
       1809 |      1,646        0.90        9.01
       1810 |      1,646        0.90        9.91
       1811 |      1,646        0.90       10.81
       1812 |      1,646        0.90       11.71
       1813 |      1,646        0.90       12.61
       1814 |      1,646        0.90       13.51
       1815 |      1,646        0.90       14.41
       1816 |      1,646        0.90       15.32
       1817 |      1,646        0.90       16.22
       1818 |      1,646        0.90       17.12
       1819 |      1,646        0.90       18.02
       1820 |      1,646        0.90       18.92
       1821 |      1,646        0.90       19.82
       1822 |      1,646        0.90       20.72
       1823 |      1,646        0.90       21.62
       1824 |      1,646        0.90       22.52
       1825 |      1,646        0.90       23.42
       1826 |      1,646        0.90       24.32
       1827 |      1,646        0.90       25.23
       1828 |      1,646        0.90       26.13
       1829 |      1,646        0.90       27.03
       1830 |      1,646        0.90       27.93
       1831 |      1,646        0.90       28.83
       1832 |      1,646        0.90       29.73
       1833 |      1,646        0.90       30.63
       1834 |      1,646        0.90       31.53
       1835 |      1,646        0.90       32.43
       1836 |      1,646        0.90       33.33
       1837 |      1,646        0.90       34.23
       1838 |      1,646        0.90       35.14
       1839 |      1,646        0.90       36.04
       1840 |      1,646        0.90       36.94
       1841 |      1,646        0.90       37.84
       1842 |      1,646        0.90       38.74
       1843 |      1,646        0.90       39.64
       1844 |      1,646        0.90       40.54
       1845 |      1,646        0.90       41.44
       1846 |      1,646        0.90       42.34
       1847 |      1,646        0.90       43.24
       1848 |      1,646        0.90       44.14
       1849 |      1,646        0.90       45.05
       1850 |      1,646        0.90       45.95
       1851 |      1,646        0.90       46.85
       1852 |      1,646        0.90       47.75
       1853 |      1,646        0.90       48.65
       1854 |      1,646        0.90       49.55
       1855 |      1,646        0.90       50.45
       1856 |      1,646        0.90       51.35
       1857 |      1,646        0.90       52.25
       1858 |      1,646        0.90       53.15
       1859 |      1,646        0.90       54.05
       1860 |      1,646        0.90       54.95
       1861 |      1,646        0.90       55.86
       1862 |      1,646        0.90       56.76
       1863 |      1,646        0.90       57.66
       1864 |      1,646        0.90       58.56
       1865 |      1,646        0.90       59.46
       1866 |      1,646        0.90       60.36
       1867 |      1,646        0.90       61.26
       1868 |      1,646        0.90       62.16
       1869 |      1,646        0.90       63.06
       1870 |      1,646        0.90       63.96
       1871 |      1,646        0.90       64.86
       1872 |      1,646        0.90       65.77
       1873 |      1,646        0.90       66.67
       1874 |      1,646        0.90       67.57
       1875 |      1,646        0.90       68.47
       1876 |      1,646        0.90       69.37
       1877 |      1,646        0.90       70.27
       1878 |      1,646        0.90       71.17
       1879 |      1,646        0.90       72.07
       1880 |      1,646        0.90       72.97
       1881 |      1,646        0.90       73.87
       1882 |      1,646        0.90       74.77
       1883 |      1,646        0.90       75.68
       1884 |      1,646        0.90       76.58
       1885 |      1,646        0.90       77.48
       1886 |      1,646        0.90       78.38
       1887 |      1,646        0.90       79.28
       1888 |      1,646        0.90       80.18
       1889 |      1,646        0.90       81.08
       1890 |      1,646        0.90       81.98
       1891 |      1,646        0.90       82.88
       1892 |      1,646        0.90       83.78
       1893 |      1,646        0.90       84.68
       1894 |      1,646        0.90       85.59
       1895 |      1,646        0.90       86.49
       1896 |      1,646        0.90       87.39
       1897 |      1,646        0.90       88.29
       1898 |      1,646        0.90       89.19
       1899 |      1,646        0.90       90.09
       1900 |      1,646        0.90       90.99
       1901 |      1,646        0.90       91.89
       1902 |      1,646        0.90       92.79
       1903 |      1,646        0.90       93.69
       1904 |      1,646        0.90       94.59
       1905 |      1,646        0.90       95.50
       1906 |      1,646        0.90       96.40
       1907 |      1,646        0.90       97.30
       1908 |      1,646        0.90       98.20
       1909 |      1,646        0.90       99.10
       1910 |      1,646        0.90      100.00
------------+-----------------------------------
      Total |    182,706      100.00

. 
. 
. foreach x of varlist year2-year111 {
  2. gen hunanX`x'=hunan*`x'
  3. 
. 
. }

. 
. 
. ***************************************************************
. foreach x of varlist year2-year111 {
  2. gen Zeng_all0_invdistX`x'=Zeng_all0_invdist*`x'
  3. }

. 
. ********
. foreach x of varlist year2-year111 {
  2. gen hXZeng_all0_invdistX`x'=hXZeng_all0_invdist*`x'
  3. }

. 
. 
. ********
. foreach x of varlist year2-year111 {
  2. gen nhXZeng_all0_invdistX`x'=nhXZeng_all0_invdist*`x'
  3. }

. 
. 
. ***************************************************************
. foreach x of varlist year2-year111 {
  2. gen Zenghu_all_invdistX`x'=Zenghu_all_invdist*`x'
  3. }

. 
. ********
. foreach x of varlist year2-year111 {
  2. gen hXZenghu_all_invdistX`x'=hXZenghu_all_invdist*`x'
  3. }

. 
. 
. ********
. foreach x of varlist year2-year111 {
  2. gen nhXZenghu_all_invdistX`x'=nhXZenghu_all_invdist*`x'
  3. }

. 
. 
. 
. ********************************************************************************
. 
. 
. reghdfe entry hXZeng_all0_invdistXyear22-hXZeng_all0_invdistXyear111  Zeng_all0_invdistXyear22-Zeng_all0_invd
> istXyear111     lnurbanpopXperiod-Taiping_route1Xperiod  , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    182,706
Absorbing 2 HDFE groups                           F( 214,    254) =   28434.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2732
                                                  Adj R-squared   =     0.2653
                                                  Within R-sq.    =     0.0316
Number of clusters (prefid)  =        255         Root MSE        =     0.3006

                                              (Std. Err. adjusted for 255 clusters in prefid)
---------------------------------------------------------------------------------------------
                            |               Robust
                      entry |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
 hXZeng_all0_invdistXyear22 |    .026349   .0143443     1.84   0.067    -.0018998    .0545979
 hXZeng_all0_invdistXyear23 |   .0286069   .0179541     1.59   0.112     -.006751    .0639648
 hXZeng_all0_invdistXyear24 |   .0487153   .0202358     2.41   0.017     .0088638    .0885667
 hXZeng_all0_invdistXyear25 |   .0634707   .0292462     2.17   0.031     .0058748    .1210667
 hXZeng_all0_invdistXyear26 |   .0519932   .0259342     2.00   0.046     .0009197    .1030667
 hXZeng_all0_invdistXyear27 |   .0619288   .0304975     2.03   0.043     .0018687    .1219889
 hXZeng_all0_invdistXyear28 |   .0615267   .0304541     2.02   0.044     .0015521    .1215013
 hXZeng_all0_invdistXyear29 |    .049969   .0182148     2.74   0.007     .0140977    .0858404
 hXZeng_all0_invdistXyear30 |   .0603385   .0304208     1.98   0.048     .0004293    .1202476
 hXZeng_all0_invdistXyear31 |   .0651905    .035564     1.83   0.068    -.0048474    .1352283
 hXZeng_all0_invdistXyear32 |   .0534851   .0378487     1.41   0.159    -.0210522    .1280223
 hXZeng_all0_invdistXyear33 |   .0569429    .037403     1.52   0.129    -.0167166    .1306024
 hXZeng_all0_invdistXyear34 |   .0634448   .0360541     1.76   0.080    -.0075583    .1344478
 hXZeng_all0_invdistXyear35 |   .0509585   .0226946     2.25   0.026     .0062649    .0956522
 hXZeng_all0_invdistXyear36 |   .0594899   .0269272     2.21   0.028     .0064608     .112519
 hXZeng_all0_invdistXyear37 |   .0587377   .0307514     1.91   0.057    -.0018225    .1192979
 hXZeng_all0_invdistXyear38 |   .0472912   .0331237     1.43   0.155    -.0179408    .1125232
 hXZeng_all0_invdistXyear39 |   .0533635   .0319823     1.67   0.096    -.0096208    .1163479
 hXZeng_all0_invdistXyear40 |   .0510685   .0408457     1.25   0.212    -.0293708    .1315078
 hXZeng_all0_invdistXyear41 |   .0495194   .0407624     1.21   0.226    -.0307559    .1297948
 hXZeng_all0_invdistXyear42 |   .0578679    .037392     1.55   0.123      -.01577    .1315058
 hXZeng_all0_invdistXyear43 |    .054921   .0378806     1.45   0.148     -.019679     .129521
 hXZeng_all0_invdistXyear44 |   .0471228    .028872     1.63   0.104    -.0097362    .1039818
 hXZeng_all0_invdistXyear45 |   .0434998   .0278599     1.56   0.120     -.011366    .0983655
 hXZeng_all0_invdistXyear46 |   .0597067   .0364692     1.64   0.103    -.0121139    .1315273
 hXZeng_all0_invdistXyear47 |    .047369   .0402664     1.18   0.241    -.0319296    .1266676
 hXZeng_all0_invdistXyear48 |   .0606075   .0361364     1.68   0.095    -.0105576    .1317725
 hXZeng_all0_invdistXyear49 |   .0507989   .0321285     1.58   0.115    -.0124733     .114071
 hXZeng_all0_invdistXyear50 |   .0356031   .0291524     1.22   0.223    -.0218081    .0930143
 hXZeng_all0_invdistXyear51 |   .0495224   .0260577     1.90   0.059    -.0017943    .1008391
 hXZeng_all0_invdistXyear52 |   .0504345   .0262288     1.92   0.056    -.0012192    .1020882
 hXZeng_all0_invdistXyear53 |   .0483698   .0289391     1.67   0.096    -.0086215     .105361
 hXZeng_all0_invdistXyear54 |   .0532061   .0259179     2.05   0.041     .0021649    .1042474
 hXZeng_all0_invdistXyear55 |   .0565615   .0310555     1.82   0.070    -.0045977    .1177206
 hXZeng_all0_invdistXyear56 |   .0410595   .0219518     1.87   0.063    -.0021712    .0842903
 hXZeng_all0_invdistXyear57 |   .0513317   .0268287     1.91   0.057    -.0015033    .1041667
 hXZeng_all0_invdistXyear58 |   .0667301   .0266837     2.50   0.013     .0141805    .1192796
 hXZeng_all0_invdistXyear59 |   .0505558   .0187258     2.70   0.007     .0136782    .0874334
 hXZeng_all0_invdistXyear60 |   .0809064   .0262631     3.08   0.002     .0291852    .1326275
 hXZeng_all0_invdistXyear61 |   .0622524   .0219927     2.83   0.005     .0189411    .1055636
 hXZeng_all0_invdistXyear62 |   .1406071   .0233137     6.03   0.000     .0946943    .1865198
 hXZeng_all0_invdistXyear63 |   .1580733   .0257754     6.13   0.000     .1073125    .2088341
 hXZeng_all0_invdistXyear64 |   .2131011   .0293817     7.25   0.000     .1552384    .2709638
 hXZeng_all0_invdistXyear65 |    .177852   .0339393     5.24   0.000     .1110138    .2446902
 hXZeng_all0_invdistXyear66 |   .1658787   .0358682     4.62   0.000     .0952418    .2365157
 hXZeng_all0_invdistXyear67 |   .1993719   .0416703     4.78   0.000     .1173086    .2814351
 hXZeng_all0_invdistXyear68 |   .1329634   .0442218     3.01   0.003     .0458754    .2200514
 hXZeng_all0_invdistXyear69 |   .1485934   .0402978     3.69   0.000     .0692331    .2279537
 hXZeng_all0_invdistXyear70 |   .1297819   .0404126     3.21   0.001     .0501953    .2093684
 hXZeng_all0_invdistXyear71 |   .1135503    .036584     3.10   0.002     .0415037     .185597
 hXZeng_all0_invdistXyear72 |   .1025891   .0374523     2.74   0.007     .0288325    .1763456
 hXZeng_all0_invdistXyear73 |   .1090886   .0363022     3.01   0.003      .037597    .1805802
 hXZeng_all0_invdistXyear74 |   .1147658   .0366377     3.13   0.002     .0426135    .1869182
 hXZeng_all0_invdistXyear75 |   .1238625   .0322088     3.85   0.000     .0604323    .1872928
 hXZeng_all0_invdistXyear76 |   .0990211   .0312082     3.17   0.002     .0375614    .1604809
 hXZeng_all0_invdistXyear77 |   .0660282   .0324993     2.03   0.043     .0020258    .1300306
 hXZeng_all0_invdistXyear78 |    .081142   .0334936     2.42   0.016     .0151814    .1471026
 hXZeng_all0_invdistXyear79 |   .1187734    .040232     2.95   0.003     .0395427    .1980041
 hXZeng_all0_invdistXyear80 |   .0995965   .0426664     2.33   0.020     .0155715    .1836215
 hXZeng_all0_invdistXyear81 |   .1290316   .0371982     3.47   0.001     .0557755    .2022878
 hXZeng_all0_invdistXyear82 |   .1632859   .0387826     4.21   0.000     .0869095    .2396624
 hXZeng_all0_invdistXyear83 |   .1422435   .0368924     3.86   0.000     .0695896    .2148973
 hXZeng_all0_invdistXyear84 |   .1574668   .0378055     4.17   0.000     .0830147    .2319189
 hXZeng_all0_invdistXyear85 |   .1772545   .0387979     4.57   0.000      .100848     .253661
 hXZeng_all0_invdistXyear86 |   .1542531   .0421151     3.66   0.000     .0713138    .2371924
 hXZeng_all0_invdistXyear87 |   .1279396   .0395707     3.23   0.001     .0500112     .205868
 hXZeng_all0_invdistXyear88 |   .1503756   .0405298     3.71   0.000     .0705582     .230193
 hXZeng_all0_invdistXyear89 |   .1262871   .0433401     2.91   0.004     .0409353    .2116389
 hXZeng_all0_invdistXyear90 |   .1126229   .0377854     2.98   0.003     .0382102    .1870355
 hXZeng_all0_invdistXyear91 |   .1142062   .0449346     2.54   0.012     .0257144    .2026979
 hXZeng_all0_invdistXyear92 |   .0590598   .0397942     1.48   0.139    -.0193087    .1374284
 hXZeng_all0_invdistXyear93 |   .0742535   .0395069     1.88   0.061    -.0035493    .1520563
 hXZeng_all0_invdistXyear94 |   .0724512   .0364591     1.99   0.048     .0006506    .1442519
 hXZeng_all0_invdistXyear95 |   .0753274   .0436055     1.73   0.085    -.0105469    .1612017
 hXZeng_all0_invdistXyear96 |   .0894233   .0407111     2.20   0.029      .009249    .1695977
 hXZeng_all0_invdistXyear97 |   .0829571   .0392049     2.12   0.035      .005749    .1601652
 hXZeng_all0_invdistXyear98 |   .0595075   .0324639     1.83   0.068    -.0044251    .1234401
 hXZeng_all0_invdistXyear99 |   .0363301   .0142647     2.55   0.011      .008238    .0644222
hXZeng_all0_invdistXyear100 |   .0516656   .0232781     2.22   0.027      .005823    .0975082
hXZeng_all0_invdistXyear101 |   .0280089   .0195185     1.43   0.153    -.0104297    .0664476
hXZeng_all0_invdistXyear102 |   .0269465   .0191872     1.40   0.161    -.0108398    .0647328
hXZeng_all0_invdistXyear103 |   .0715813   .0270567     2.65   0.009     .0182971    .1248654
hXZeng_all0_invdistXyear104 |   .0636451   .0378461     1.68   0.094     -.010887    .1381771
hXZeng_all0_invdistXyear105 |   .0696085   .0332596     2.09   0.037     .0041088    .1351082
hXZeng_all0_invdistXyear106 |   .0548991    .029428     1.87   0.063    -.0030549    .1128532
hXZeng_all0_invdistXyear107 |   .0415801     .03432     1.21   0.227     -.026008    .1091682
hXZeng_all0_invdistXyear108 |   .0099219      .0253     0.39   0.695    -.0399026    .0597465
hXZeng_all0_invdistXyear109 |   .0526275   .0325528     1.62   0.107    -.0114802    .1167353
hXZeng_all0_invdistXyear110 |   .0462352   .0299523     1.54   0.124    -.0127513    .1052217
hXZeng_all0_invdistXyear111 |   .0248594   .0305847     0.81   0.417    -.0353725    .0850912
   Zeng_all0_invdistXyear22 |  -.0257689   .0150625    -1.71   0.088    -.0554323    .0038945
   Zeng_all0_invdistXyear23 |  -.0327681   .0196369    -1.67   0.096    -.0714401    .0059038
   Zeng_all0_invdistXyear24 |  -.0513542   .0221923    -2.31   0.021    -.0950586   -.0076499
   Zeng_all0_invdistXyear25 |  -.0672791   .0319208    -2.11   0.036    -.1301422    -.004416
   Zeng_all0_invdistXyear26 |  -.0563566   .0282314    -2.00   0.047     -.111954   -.0007592
   Zeng_all0_invdistXyear27 |  -.0654654   .0331582    -1.97   0.049    -.1307654   -.0001655
   Zeng_all0_invdistXyear28 |  -.0648091   .0331576    -1.95   0.052     -.130108    .0004898
   Zeng_all0_invdistXyear29 |  -.0389636   .0190504    -2.05   0.042    -.0764805   -.0014468
   Zeng_all0_invdistXyear30 |  -.0635982   .0331182    -1.92   0.056    -.1288194     .001623
   Zeng_all0_invdistXyear31 |  -.0690789   .0387304    -1.78   0.076    -.1453525    .0071947
   Zeng_all0_invdistXyear32 |  -.0587474   .0411745    -1.43   0.155    -.1398343    .0223394
   Zeng_all0_invdistXyear33 |  -.0589239   .0406842    -1.45   0.149    -.1390452    .0211974
   Zeng_all0_invdistXyear34 |  -.0678831   .0392894    -1.73   0.085    -.1452576    .0094915
   Zeng_all0_invdistXyear35 |  -.0437172   .0244333    -1.79   0.075    -.0918347    .0044004
   Zeng_all0_invdistXyear36 |  -.0522408   .0291861    -1.79   0.075    -.1097185    .0052368
   Zeng_all0_invdistXyear37 |   -.062563    .033464    -1.87   0.063    -.1284653    .0033394
   Zeng_all0_invdistXyear38 |  -.0520965   .0359598    -1.45   0.149     -.122914    .0187209
   Zeng_all0_invdistXyear39 |  -.0574528   .0347602    -1.65   0.100    -.1259078    .0110021
   Zeng_all0_invdistXyear40 |  -.0494999   .0443347    -1.12   0.265    -.1368104    .0378106
   Zeng_all0_invdistXyear41 |  -.0503927   .0441829    -1.14   0.255    -.1374041    .0366188
   Zeng_all0_invdistXyear42 |  -.0615994   .0406835    -1.51   0.131    -.1417194    .0185207
   Zeng_all0_invdistXyear43 |  -.0594946   .0411618    -1.45   0.150    -.1405566    .0215673
   Zeng_all0_invdistXyear44 |  -.0402272   .0312404    -1.29   0.199    -.1017505     .021296
   Zeng_all0_invdistXyear45 |  -.0472635   .0302798    -1.56   0.120     -.106895     .012368
   Zeng_all0_invdistXyear46 |  -.0641637   .0396738    -1.62   0.107    -.1422952    .0139678
   Zeng_all0_invdistXyear47 |  -.0486461   .0435426    -1.12   0.265    -.1343966    .0371044
   Zeng_all0_invdistXyear48 |  -.0655834   .0393191    -1.67   0.097    -.1430164    .0118495
   Zeng_all0_invdistXyear49 |   -.055877   .0349039    -1.60   0.111     -.124615    .0128609
   Zeng_all0_invdistXyear50 |  -.0413624   .0316463    -1.31   0.192    -.1036849      .02096
   Zeng_all0_invdistXyear51 |  -.0518758   .0295343    -1.76   0.080     -.110039    .0062875
   Zeng_all0_invdistXyear52 |  -.0515336   .0296076    -1.74   0.083    -.1098414    .0067741
   Zeng_all0_invdistXyear53 |  -.0464295   .0318825    -1.46   0.147    -.1092173    .0163583
   Zeng_all0_invdistXyear54 |  -.0541824   .0293125    -1.85   0.066    -.1119088     .003544
   Zeng_all0_invdistXyear55 |  -.0572282   .0345769    -1.66   0.099    -.1253222    .0108658
   Zeng_all0_invdistXyear56 |  -.0410036   .0244679    -1.68   0.095    -.0891893    .0071822
   Zeng_all0_invdistXyear57 |  -.0490273   .0296802    -1.65   0.100    -.1074779    .0094233
   Zeng_all0_invdistXyear58 |  -.0518727   .0291812    -1.78   0.077    -.1093406    .0055952
   Zeng_all0_invdistXyear59 |  -.0368886   .0196745    -1.87   0.062    -.0756344    .0018573
   Zeng_all0_invdistXyear60 |  -.0554733   .0282814    -1.96   0.051    -.1111692    .0002226
   Zeng_all0_invdistXyear61 |   -.048283   .0235621    -2.05   0.041    -.0946849   -.0018811
   Zeng_all0_invdistXyear62 |  -.0419411   .0244533    -1.72   0.088    -.0900981    .0062158
   Zeng_all0_invdistXyear63 |  -.0393744   .0262546    -1.50   0.135    -.0910788      .01233
   Zeng_all0_invdistXyear64 |  -.0398767   .0258202    -1.54   0.124    -.0907258    .0109723
   Zeng_all0_invdistXyear65 |  -.0395836   .0330478    -1.20   0.232    -.1046661     .025499
   Zeng_all0_invdistXyear66 |  -.0488304   .0384847    -1.27   0.206    -.1246201    .0269594
   Zeng_all0_invdistXyear67 |  -.0580569   .0427706    -1.36   0.176    -.1422871    .0261733
   Zeng_all0_invdistXyear68 |  -.0413289   .0475197    -0.87   0.385    -.1349117    .0522538
   Zeng_all0_invdistXyear69 |  -.0559838   .0436066    -1.28   0.200    -.1418604    .0298927
   Zeng_all0_invdistXyear70 |  -.0561415    .043175    -1.30   0.195    -.1411681    .0288851
   Zeng_all0_invdistXyear71 |  -.0454864   .0392468    -1.16   0.248    -.1227769    .0318041
   Zeng_all0_invdistXyear72 |  -.0626583   .0410713    -1.53   0.128    -.1435419    .0182254
   Zeng_all0_invdistXyear73 |  -.0656773   .0403243    -1.63   0.105    -.1450899    .0137353
   Zeng_all0_invdistXyear74 |  -.0674871   .0397227    -1.70   0.091    -.1457149    .0107408
   Zeng_all0_invdistXyear75 |  -.0632458   .0342485    -1.85   0.066     -.130693    .0042015
   Zeng_all0_invdistXyear76 |  -.0422178    .033676    -1.25   0.211    -.1085375     .024102
   Zeng_all0_invdistXyear77 |  -.0364858   .0358193    -1.02   0.309    -.1070265    .0340548
   Zeng_all0_invdistXyear78 |  -.0520513    .037257    -1.40   0.164    -.1254232    .0213207
   Zeng_all0_invdistXyear79 |  -.0548965   .0438605    -1.25   0.212    -.1412731    .0314801
   Zeng_all0_invdistXyear80 |  -.0446418   .0466855    -0.96   0.340    -.1365817    .0472982
   Zeng_all0_invdistXyear81 |  -.0633327   .0409633    -1.55   0.123    -.1440038    .0173384
   Zeng_all0_invdistXyear82 |  -.0563996   .0429746    -1.31   0.191    -.1410315    .0282323
   Zeng_all0_invdistXyear83 |  -.0594121   .0411512    -1.44   0.150    -.1404531     .021629
   Zeng_all0_invdistXyear84 |  -.0596656   .0417454    -1.43   0.154    -.1418768    .0225456
   Zeng_all0_invdistXyear85 |  -.0573568   .0426171    -1.35   0.180    -.1412847     .026571
   Zeng_all0_invdistXyear86 |  -.0466248   .0468218    -1.00   0.320    -.1388331    .0455835
   Zeng_all0_invdistXyear87 |  -.0556579   .0439477    -1.27   0.207    -.1422062    .0308903
   Zeng_all0_invdistXyear88 |  -.0538791   .0452227    -1.19   0.235    -.1429383    .0351801
   Zeng_all0_invdistXyear89 |   -.038397   .0420789    -0.91   0.362    -.1212651     .044471
   Zeng_all0_invdistXyear90 |  -.0288025   .0393718    -0.73   0.465    -.1063391    .0487342
   Zeng_all0_invdistXyear91 |  -.0474479   .0473977    -1.00   0.318    -.1407905    .0458947
   Zeng_all0_invdistXyear92 |  -.0372253   .0440883    -0.84   0.399    -.1240506    .0495999
   Zeng_all0_invdistXyear93 |  -.0573711   .0441179    -1.30   0.195    -.1442547    .0295124
   Zeng_all0_invdistXyear94 |   -.049232    .038512    -1.28   0.202    -.1250756    .0266116
   Zeng_all0_invdistXyear95 |  -.0489871   .0457415    -1.07   0.285     -.139068    .0410938
   Zeng_all0_invdistXyear96 |  -.0555081   .0436846    -1.27   0.205    -.1415383    .0305222
   Zeng_all0_invdistXyear97 |  -.0597048   .0421042    -1.42   0.157    -.1426225     .023213
   Zeng_all0_invdistXyear98 |  -.0409507   .0335757    -1.22   0.224     -.107073    .0251715
   Zeng_all0_invdistXyear99 |  -.0179882    .012987    -1.39   0.167    -.0435641    .0075877
  Zeng_all0_invdistXyear100 |  -.0328745   .0151903    -2.16   0.031    -.0627894   -.0029596
  Zeng_all0_invdistXyear101 |  -.0118186   .0140525    -0.84   0.401    -.0394928    .0158556
  Zeng_all0_invdistXyear102 |  -.0004139   .0125474    -0.03   0.974     -.025124    .0242963
  Zeng_all0_invdistXyear103 |  -.0375163   .0283556    -1.32   0.187    -.0933583    .0183258
  Zeng_all0_invdistXyear104 |  -.0431578    .040669    -1.06   0.290    -.1232492    .0369335
  Zeng_all0_invdistXyear105 |  -.0529633   .0374367    -1.41   0.158    -.1266891    .0207624
  Zeng_all0_invdistXyear106 |  -.0444041   .0330377    -1.34   0.180    -.1094669    .0206587
  Zeng_all0_invdistXyear107 |  -.0304295    .037595    -0.81   0.419    -.1044671    .0436081
  Zeng_all0_invdistXyear108 |   .0120445   .0269725     0.45   0.656    -.0410737    .0651628
  Zeng_all0_invdistXyear109 |   -.016471   .0354199    -0.47   0.642     -.086225    .0532831
  Zeng_all0_invdistXyear110 |  -.0249341   .0327987    -0.76   0.448    -.0895261    .0396579
  Zeng_all0_invdistXyear111 |  -.0058564   .0332782    -0.18   0.860    -.0713927    .0596799
          lnurbanpopXperiod |   .0051726   .0028939     1.79   0.075    -.0005264    .0108717
            prefcapXperiod1 |  -.0112594   .0219229    -0.51   0.608    -.0544332    .0319145
            prefcapXperiod2 |   .0021938   .0317168     0.07   0.945    -.0602675    .0646551
             prefcapXperiod |    .003633   .0200985     0.18   0.857    -.0359479    .0432139
           lnjinshiXperiod1 |  -.0130734   .0097889    -1.34   0.183    -.0323511    .0062042
           lnjinshiXperiod2 |   .0046861   .0113106     0.41   0.679    -.0175883    .0269606
            lnjinshiXperiod |  -.0119637   .0090883    -1.32   0.189    -.0298617    .0059344
       lncntyquota0Xperiod1 |  -.0170766   .0118522    -1.44   0.151    -.0404178    .0062645
       lncntyquota0Xperiod2 |  -.0275333   .0171829    -1.60   0.110    -.0613725    .0063059
        lncntyquota0Xperiod |   .0072732   .0105139     0.69   0.490    -.0134323    .0279786
          lncntypopXperiod1 |   .0146622   .0093316     1.57   0.117    -.0037149    .0330393
          lncntypopXperiod2 |   .0268812   .0167385     1.61   0.110    -.0060829    .0598452
           lncntypopXperiod |   .0018684   .0084347     0.22   0.825    -.0147425    .0184793
         lncntyareaXperiod1 |   .0016539   .0088689     0.19   0.852     -.015812    .0191198
         lncntyareaXperiod2 |   .0110866    .012494     0.89   0.376    -.0135184    .0356917
          lncntyareaXperiod |  -.0097614   .0076603    -1.27   0.204    -.0248472    .0053245
            mainrivXperiod1 |   .0068655   .0108561     0.63   0.528    -.0145139     .028245
            mainrivXperiod2 |   .0139603   .0170758     0.82   0.414     -.019668    .0475885
             mainrivXperiod |   -.002849   .0108276    -0.26   0.793    -.0241723    .0184743
         dist2canalXperiod1 |   -.003764   .0029733    -1.27   0.207    -.0096195    .0020915
         dist2canalXperiod2 |   .0048033   .0052113     0.92   0.358    -.0054594    .0150661
          dist2canalXperiod |   .0033388   .0032135     1.04   0.300    -.0029898    .0096674
             lnriceXperiod1 |   .0118996   .0182571     0.65   0.515     -.024055    .0478542
             lnriceXperiod2 |  -.0147147   .0357106    -0.41   0.681    -.0850412    .0556119
              lnriceXperiod |  -.0017792   .0188882    -0.09   0.925    -.0389767    .0354182
            lnwheatXperiod1 |   .0231811   .0144961     1.60   0.111    -.0053668    .0517289
            lnwheatXperiod2 |   .0055292   .0196518     0.28   0.779     -.033172    .0442305
             lnwheatXperiod |  -.0199812   .0165369    -1.21   0.228    -.0525482    .0125858
       dist_nanjingXperiod1 |   .0027845   .0039371     0.71   0.480     -.004969     .010538
       dist_nanjingXperiod2 |  -.0068571   .0075285    -0.91   0.363    -.0216833    .0079692
        dist_nanjingXperiod |  -.0022589   .0040277    -0.56   0.575    -.0101908    .0056729
     Taiping_route1Xperiod1 |  -.0006479   .0269531    -0.02   0.981     -.053728    .0524322
     Taiping_route1Xperiod2 |   .0129665   .0468674     0.28   0.782    -.0793317    .1052646
      Taiping_route1Xperiod |  -.0187163     .05359    -0.35   0.727    -.1242537    .0868211
                      _cons |  -.0629572   .0637294    -0.99   0.324    -.1884626    .0625481
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |       111           0         111     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. parmest, saving( Results\ConnectionOnEntry_all0_yearly_0, replace)
file Results\ConnectionOnEntry_all0_yearly_0.dta saved

. 
. 
. reghdfe reentry hXZeng_all0_invdistXyear22-hXZeng_all0_invdistXyear111  Zeng_all0_invdistXyear22-Zeng_all0_in
> vdistXyear111     lnurbanpopXperiod-Taiping_route1Xperiod  , absorb(year samcntyid  ) cluster(prefid )
(MWFE estimator converged in 2 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =    182,706
Absorbing 2 HDFE groups                           F( 214,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.2778
                                                  Adj R-squared   =     0.2699
                                                  Within R-sq.    =     0.0201
Number of clusters (prefid)  =        255         Root MSE        =     0.2971

                                              (Std. Err. adjusted for 255 clusters in prefid)
---------------------------------------------------------------------------------------------
                            |               Robust
                    reentry |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
 hXZeng_all0_invdistXyear22 |  -.0175934   .0142119    -1.24   0.217    -.0455816    .0103948
 hXZeng_all0_invdistXyear23 |   .0133481   .0201364     0.66   0.508    -.0263075    .0530037
 hXZeng_all0_invdistXyear24 |   .0150501   .0178569     0.84   0.400    -.0201163    .0502166
 hXZeng_all0_invdistXyear25 |   .0055044   .0083424     0.66   0.510    -.0109247    .0219336
 hXZeng_all0_invdistXyear26 |   .0077928   .0145654     0.54   0.593    -.0208915     .036477
 hXZeng_all0_invdistXyear27 |  -.0030093   .0127956    -0.24   0.814    -.0282082    .0221896
 hXZeng_all0_invdistXyear28 |  -.0017873     .01595    -0.11   0.911    -.0331983    .0296238
 hXZeng_all0_invdistXyear29 |  -.0298721   .0215618    -1.39   0.167    -.0723349    .0125906
 hXZeng_all0_invdistXyear30 |  -.0163567   .0123987    -1.32   0.188    -.0407741    .0080606
 hXZeng_all0_invdistXyear31 |  -.0245909   .0214746    -1.15   0.253    -.0668818       .0177
 hXZeng_all0_invdistXyear32 |  -.0208747     .01818    -1.15   0.252    -.0566774    .0149279
 hXZeng_all0_invdistXyear33 |  -.0235489   .0145769    -1.62   0.107    -.0522558    .0051581
 hXZeng_all0_invdistXyear34 |  -.0171005   .0167336    -1.02   0.308    -.0500547    .0158538
 hXZeng_all0_invdistXyear35 |  -.0304869   .0177063    -1.72   0.086    -.0653569     .004383
 hXZeng_all0_invdistXyear36 |  -.0259075   .0154392    -1.68   0.095    -.0563127    .0044977
 hXZeng_all0_invdistXyear37 |  -.0367631   .0115975    -3.17   0.002    -.0596026   -.0139235
 hXZeng_all0_invdistXyear38 |  -.0238265   .0111732    -2.13   0.034    -.0458305   -.0018225
 hXZeng_all0_invdistXyear39 |  -.0222406   .0109696    -2.03   0.044    -.0438435   -.0006376
 hXZeng_all0_invdistXyear40 |  -.0325846   .0147245    -2.21   0.028    -.0615822    -.003587
 hXZeng_all0_invdistXyear41 |  -.0313776   .0143628    -2.18   0.030    -.0596628   -.0030923
 hXZeng_all0_invdistXyear42 |   .0000881   .0114047     0.01   0.994    -.0223717     .022548
 hXZeng_all0_invdistXyear43 |  -.0122638   .0106408    -1.15   0.250    -.0332193    .0086917
 hXZeng_all0_invdistXyear44 |   -.025803   .0133982    -1.93   0.055    -.0521887    .0005827
 hXZeng_all0_invdistXyear45 |  -.0160802   .0174231    -0.92   0.357    -.0503923    .0182319
 hXZeng_all0_invdistXyear46 |  -.0239222   .0198671    -1.20   0.230    -.0630475     .015203
 hXZeng_all0_invdistXyear47 |  -.0128282   .0142595    -0.90   0.369      -.04091    .0152537
 hXZeng_all0_invdistXyear48 |   -.005308   .0202623    -0.26   0.794    -.0452114    .0345955
 hXZeng_all0_invdistXyear49 |   -.006459   .0187353    -0.34   0.731    -.0433554    .0304374
 hXZeng_all0_invdistXyear50 |   .0085562   .0200512     0.43   0.670    -.0309316    .0480441
 hXZeng_all0_invdistXyear51 |   .0014688   .0141486     0.10   0.917    -.0263946    .0293322
 hXZeng_all0_invdistXyear52 |  -.0245537   .0197373    -1.24   0.215    -.0634234    .0143159
 hXZeng_all0_invdistXyear53 |  -.0108468   .0299666    -0.36   0.718    -.0698614    .0481679
 hXZeng_all0_invdistXyear54 |  -.0241268   .0221947    -1.09   0.278    -.0678358    .0195822
 hXZeng_all0_invdistXyear55 |   .0019741   .0159234     0.12   0.901    -.0293846    .0333329
 hXZeng_all0_invdistXyear56 |   .0024297   .0157477     0.15   0.878     -.028583    .0334424
 hXZeng_all0_invdistXyear57 |   -.003853    .009669    -0.40   0.691    -.0228946    .0151886
 hXZeng_all0_invdistXyear58 |  -.0013808   .0082831    -0.17   0.868    -.0176931    .0149315
 hXZeng_all0_invdistXyear59 |  -.0233528   .0113487    -2.06   0.041    -.0457023   -.0010033
 hXZeng_all0_invdistXyear60 |  -.0281999   .0115119    -2.45   0.015    -.0508708    -.005529
 hXZeng_all0_invdistXyear61 |  -.0274596   .0153389    -1.79   0.075    -.0576673     .002748
 hXZeng_all0_invdistXyear62 |   .0020902   .0121492     0.17   0.864    -.0218358    .0260162
 hXZeng_all0_invdistXyear63 |  -.0021074   .0192561    -0.11   0.913    -.0400292    .0358145
 hXZeng_all0_invdistXyear64 |  -.0196352   .0275114    -0.71   0.476    -.0738147    .0345443
 hXZeng_all0_invdistXyear65 |   .0061499   .0116277     0.53   0.597    -.0167491    .0290489
 hXZeng_all0_invdistXyear66 |   .0105881   .0154758     0.68   0.494    -.0198892    .0410654
 hXZeng_all0_invdistXyear67 |   .0414415   .0118948     3.48   0.001     .0180165    .0648665
 hXZeng_all0_invdistXyear68 |   .0217776   .0303325     0.72   0.473    -.0379575    .0815128
 hXZeng_all0_invdistXyear69 |   .0073749    .042499     0.17   0.862    -.0763204    .0910702
 hXZeng_all0_invdistXyear70 |   .0074449   .0293287     0.25   0.800    -.0503134    .0652033
 hXZeng_all0_invdistXyear71 |  -.0146392   .0285087    -0.51   0.608    -.0707828    .0415044
 hXZeng_all0_invdistXyear72 |  -.0335562   .0362846    -0.92   0.356    -.1050132    .0379008
 hXZeng_all0_invdistXyear73 |  -.0223506   .0324696    -0.69   0.492    -.0862945    .0415933
 hXZeng_all0_invdistXyear74 |  -.0356304   .0234255    -1.52   0.130    -.0817634    .0105026
 hXZeng_all0_invdistXyear75 |  -.0345647   .0287555    -1.20   0.230    -.0911943    .0220649
 hXZeng_all0_invdistXyear76 |    .011039    .028122     0.39   0.695     -.044343    .0664209
 hXZeng_all0_invdistXyear77 |   .0058202     .03282     0.18   0.859    -.0588138    .0704543
 hXZeng_all0_invdistXyear78 |   .0058338   .0342467     0.17   0.865    -.0616098    .0732774
 hXZeng_all0_invdistXyear79 |   .0148297   .0384752     0.39   0.700    -.0609414    .0906008
 hXZeng_all0_invdistXyear80 |  -.0174766   .0372013    -0.47   0.639    -.0907388    .0557855
 hXZeng_all0_invdistXyear81 |   -.013372   .0381311    -0.35   0.726    -.0884654    .0617215
 hXZeng_all0_invdistXyear82 |  -.0015229   .0281316    -0.05   0.957    -.0569239     .053878
 hXZeng_all0_invdistXyear83 |   .0012225   .0168736     0.07   0.942    -.0320074    .0344525
 hXZeng_all0_invdistXyear84 |   .0214196   .0116992     1.83   0.068    -.0016201    .0444593
 hXZeng_all0_invdistXyear85 |   .0260275   .0123856     2.10   0.037      .001636     .050419
 hXZeng_all0_invdistXyear86 |  -.0005754   .0162358    -0.04   0.972    -.0325493    .0313985
 hXZeng_all0_invdistXyear87 |    .000259   .0131702     0.02   0.984    -.0256776    .0261957
 hXZeng_all0_invdistXyear88 |   .0244152   .0117727     2.07   0.039     .0012306    .0475998
 hXZeng_all0_invdistXyear89 |   .0324329   .0103108     3.15   0.002     .0121273    .0527384
 hXZeng_all0_invdistXyear90 |   .0064057   .0125805     0.51   0.611    -.0183696     .031181
 hXZeng_all0_invdistXyear91 |   .0160762   .0150309     1.07   0.286    -.0135248    .0456772
 hXZeng_all0_invdistXyear92 |    .007432   .0151801     0.49   0.625    -.0224629     .037327
 hXZeng_all0_invdistXyear93 |   .0218564   .0151763     1.44   0.151    -.0080311    .0517439
 hXZeng_all0_invdistXyear94 |   .0242586   .0138202     1.76   0.080    -.0029581    .0514754
 hXZeng_all0_invdistXyear95 |   .0135828   .0124499     1.09   0.276    -.0109353     .038101
 hXZeng_all0_invdistXyear96 |   .0391016    .020889     1.87   0.062     -.002036    .0802393
 hXZeng_all0_invdistXyear97 |    .017913    .012644     1.42   0.158    -.0069875    .0428135
 hXZeng_all0_invdistXyear98 |  -.0028135   .0157915    -0.18   0.859    -.0339123    .0282854
 hXZeng_all0_invdistXyear99 |  -.0075794   .0152974    -0.50   0.621    -.0377054    .0225465
hXZeng_all0_invdistXyear100 |   .0126798   .0153731     0.82   0.410    -.0175953    .0429548
hXZeng_all0_invdistXyear101 |  -.0016282   .0187568    -0.09   0.931    -.0385669    .0353106
hXZeng_all0_invdistXyear102 |   .0005399   .0130388     0.04   0.967    -.0251379    .0262178
hXZeng_all0_invdistXyear103 |   .0037484   .0138733     0.27   0.787     -.023573    .0310697
hXZeng_all0_invdistXyear104 |  -.0027918   .0129815    -0.22   0.830    -.0283569    .0227733
hXZeng_all0_invdistXyear105 |    .009799   .0111361     0.88   0.380    -.0121319    .0317298
hXZeng_all0_invdistXyear106 |  -.0038057   .0137349    -0.28   0.782    -.0308544    .0232431
hXZeng_all0_invdistXyear107 |  -.0222936   .0216385    -1.03   0.304    -.0649074    .0203201
hXZeng_all0_invdistXyear108 |  -.0003507   .0140009    -0.03   0.980    -.0279234     .027222
hXZeng_all0_invdistXyear109 |  -.0038136   .0118705    -0.32   0.748    -.0271906    .0195635
hXZeng_all0_invdistXyear110 |  -.0081694   .0157502    -0.52   0.604    -.0391871    .0228483
hXZeng_all0_invdistXyear111 |  -.0104337   .0161529    -0.65   0.519    -.0422443     .021377
   Zeng_all0_invdistXyear22 |   .0374297   .0149735     2.50   0.013     .0079416    .0669178
   Zeng_all0_invdistXyear23 |   .0186932    .009364     2.00   0.047     .0002523    .0371341
   Zeng_all0_invdistXyear24 |   .0075351   .0112128     0.67   0.502    -.0145468     .029617
   Zeng_all0_invdistXyear25 |   .0114313   .0064019     1.79   0.075    -.0011763     .024039
   Zeng_all0_invdistXyear26 |   .0086823   .0102369     0.85   0.397    -.0114778    .0288423
   Zeng_all0_invdistXyear27 |   .0139703   .0062053     2.25   0.025       .00175    .0261907
   Zeng_all0_invdistXyear28 |   .0144995   .0066905     2.17   0.031     .0013235    .0276754
   Zeng_all0_invdistXyear29 |   .0406994   .0203915     2.00   0.047     .0005414    .0808575
   Zeng_all0_invdistXyear30 |   .0235486   .0114201     2.06   0.040     .0010584    .0460387
   Zeng_all0_invdistXyear31 |   .0402773   .0209665     1.92   0.056    -.0010131    .0815678
   Zeng_all0_invdistXyear32 |   .0322831   .0107377     3.01   0.003     .0111369    .0534293
   Zeng_all0_invdistXyear33 |   .0229119   .0154218     1.49   0.139    -.0074589    .0532827
   Zeng_all0_invdistXyear34 |    .029138   .0129028     2.26   0.025      .003728    .0545481
   Zeng_all0_invdistXyear35 |    .036765   .0163544     2.25   0.025     .0045575    .0689726
   Zeng_all0_invdistXyear36 |   .0320081    .013581     2.36   0.019     .0052625    .0587537
   Zeng_all0_invdistXyear37 |   .0381838    .011572     3.30   0.001     .0153946    .0609731
   Zeng_all0_invdistXyear38 |   .0246958   .0113168     2.18   0.030     .0024091    .0469825
   Zeng_all0_invdistXyear39 |   .0227279   .0108793     2.09   0.038     .0013029    .0441529
   Zeng_all0_invdistXyear40 |   .0391943   .0128102     3.06   0.002     .0139666    .0644221
   Zeng_all0_invdistXyear41 |   .0371098   .0147225     2.52   0.012      .008116    .0661037
   Zeng_all0_invdistXyear42 |   .0206459   .0097922     2.11   0.036     .0013616    .0399302
   Zeng_all0_invdistXyear43 |   .0237017   .0100145     2.37   0.019     .0039797    .0434237
   Zeng_all0_invdistXyear44 |   .0340824   .0130658     2.61   0.010     .0083514    .0598135
   Zeng_all0_invdistXyear45 |   .0328159   .0176704     1.86   0.064    -.0019833    .0676151
   Zeng_all0_invdistXyear46 |   .0392111   .0205584     1.91   0.058    -.0012755    .0796977
   Zeng_all0_invdistXyear47 |    .024116   .0141986     1.70   0.091    -.0038459    .0520779
   Zeng_all0_invdistXyear48 |   .0451184    .020681     2.18   0.030     .0043902    .0858466
   Zeng_all0_invdistXyear49 |   .0323068   .0177441     1.82   0.070    -.0026374    .0672511
   Zeng_all0_invdistXyear50 |   .0460421   .0149007     3.09   0.002     .0166974    .0753868
   Zeng_all0_invdistXyear51 |   .0218682   .0091773     2.38   0.018     .0037948    .0399416
   Zeng_all0_invdistXyear52 |   .0459943   .0215392     2.14   0.034     .0035762    .0884124
   Zeng_all0_invdistXyear53 |   .0723466   .0294154     2.46   0.015     .0144174    .1302757
   Zeng_all0_invdistXyear54 |   .0431051   .0183984     2.34   0.020     .0068723    .0793379
   Zeng_all0_invdistXyear55 |   .0327642   .0142142     2.31   0.022     .0047715    .0607568
   Zeng_all0_invdistXyear56 |   .0293556   .0175881     1.67   0.096    -.0052814    .0639926
   Zeng_all0_invdistXyear57 |   .0233616    .009097     2.57   0.011     .0054465    .0412767
   Zeng_all0_invdistXyear58 |   .0199029   .0082092     2.42   0.016     .0037361    .0360697
   Zeng_all0_invdistXyear59 |   .0293981   .0117072     2.51   0.013     .0063426    .0524536
   Zeng_all0_invdistXyear60 |   .0317281   .0122832     2.58   0.010     .0075383    .0559179
   Zeng_all0_invdistXyear61 |   .0435398    .016272     2.68   0.008     .0114945     .075585
   Zeng_all0_invdistXyear62 |   .0254386   .0123394     2.06   0.040     .0011381    .0497391
   Zeng_all0_invdistXyear63 |   .0501255   .0201553     2.49   0.014     .0104327    .0898183
   Zeng_all0_invdistXyear64 |   .0570172   .0304346     1.87   0.062    -.0029191    .1169535
   Zeng_all0_invdistXyear65 |   .0303495   .0115642     2.62   0.009     .0075757    .0531234
   Zeng_all0_invdistXyear66 |   .0286687   .0164484     1.74   0.083     -.003724    .0610613
   Zeng_all0_invdistXyear67 |   .0201733   .0091492     2.20   0.028     .0021554    .0381913
   Zeng_all0_invdistXyear68 |   .0497936   .0324701     1.53   0.126    -.0141513    .1137385
   Zeng_all0_invdistXyear69 |   .0768026   .0463372     1.66   0.099    -.0144515    .1680567
   Zeng_all0_invdistXyear70 |   .0497023   .0318956     1.56   0.120    -.0131112    .1125158
   Zeng_all0_invdistXyear71 |   .0595326   .0311232     1.91   0.057    -.0017598     .120825
   Zeng_all0_invdistXyear72 |   .0696232   .0400314     1.74   0.083    -.0092125    .1484589
   Zeng_all0_invdistXyear73 |   .0582636   .0358978     1.62   0.106    -.0124316    .1289589
   Zeng_all0_invdistXyear74 |   .0483204    .025819     1.87   0.062    -.0025262    .0991669
   Zeng_all0_invdistXyear75 |   .0468386   .0319182     1.47   0.143    -.0160195    .1096967
   Zeng_all0_invdistXyear76 |   .0503929   .0311265     1.62   0.107     -.010906    .1116918
   Zeng_all0_invdistXyear77 |   .0561744   .0363304     1.55   0.123    -.0153728    .1277217
   Zeng_all0_invdistXyear78 |   .0509508   .0378497     1.35   0.179    -.0235885    .1254901
   Zeng_all0_invdistXyear79 |   .0591537   .0424383     1.39   0.165     -.024422    .1427294
   Zeng_all0_invdistXyear80 |    .074449    .040909     1.82   0.070    -.0061151    .1550131
   Zeng_all0_invdistXyear81 |   .0660639   .0420192     1.57   0.117    -.0166866    .1488144
   Zeng_all0_invdistXyear82 |   .0514638   .0310493     1.66   0.099     -.009683    .1126105
   Zeng_all0_invdistXyear83 |   .0326421   .0172032     1.90   0.059     -.001237    .0665212
   Zeng_all0_invdistXyear84 |   .0272942   .0112715     2.42   0.016     .0050966    .0494918
   Zeng_all0_invdistXyear85 |   .0226112   .0117682     1.92   0.056    -.0005645    .0457869
   Zeng_all0_invdistXyear86 |   .0404722   .0161457     2.51   0.013     .0086757    .0722687
   Zeng_all0_invdistXyear87 |   .0398642   .0122634     3.25   0.001     .0157133     .064015
   Zeng_all0_invdistXyear88 |   .0179293   .0083983     2.13   0.034     .0013901    .0344686
   Zeng_all0_invdistXyear89 |   .0181239   .0093299     1.94   0.053    -.0002498    .0364977
   Zeng_all0_invdistXyear90 |   .0240801   .0113918     2.11   0.036     .0016456    .0465146
   Zeng_all0_invdistXyear91 |   .0269477   .0136404     1.98   0.049     .0000849    .0538104
   Zeng_all0_invdistXyear92 |   .0278537    .014453     1.93   0.055    -.0006093    .0563167
   Zeng_all0_invdistXyear93 |   .0160553   .0138401     1.16   0.247    -.0112006    .0433113
   Zeng_all0_invdistXyear94 |   .0121469   .0124807     0.97   0.331    -.0124321    .0367258
   Zeng_all0_invdistXyear95 |   .0233321   .0107106     2.18   0.030     .0022393    .0444249
   Zeng_all0_invdistXyear96 |   .0163725   .0159694     1.03   0.306    -.0150767    .0478217
   Zeng_all0_invdistXyear97 |   .0061354   .0097091     0.63   0.528    -.0129852     .025256
   Zeng_all0_invdistXyear98 |   .0191583    .011157     1.72   0.087    -.0028137    .0411303
   Zeng_all0_invdistXyear99 |   .0211997   .0123336     1.72   0.087    -.0030895    .0454889
  Zeng_all0_invdistXyear100 |   .0008678   .0084361     0.10   0.918    -.0157459    .0174815
  Zeng_all0_invdistXyear101 |   .0136606   .0150466     0.91   0.365    -.0159714    .0432927
  Zeng_all0_invdistXyear102 |   .0055198   .0112354     0.49   0.624    -.0166067    .0276462
  Zeng_all0_invdistXyear103 |   .0040652   .0111344     0.37   0.715    -.0178622    .0259927
  Zeng_all0_invdistXyear104 |   .0090931   .0123233     0.74   0.461    -.0151757     .033362
  Zeng_all0_invdistXyear105 |  -.0028397   .0074667    -0.38   0.704    -.0175442    .0118648
  Zeng_all0_invdistXyear106 |   .0088729   .0125967     0.70   0.482    -.0159344    .0336803
  Zeng_all0_invdistXyear107 |   .0246154   .0220455     1.12   0.265    -.0187998    .0680306
  Zeng_all0_invdistXyear108 |   .0054549   .0130078     0.42   0.675     -.020162    .0310719
  Zeng_all0_invdistXyear109 |   .0066915   .0111798     0.60   0.550    -.0153253    .0287084
  Zeng_all0_invdistXyear110 |   .0119389   .0157922     0.76   0.450    -.0191614    .0430392
  Zeng_all0_invdistXyear111 |   .0140255    .016234     0.86   0.388    -.0179448    .0459959
          lnurbanpopXperiod |    .004611   .0016058     2.87   0.004     .0014487    .0077734
            prefcapXperiod1 |  -.0059638   .0263532    -0.23   0.821    -.0578624    .0459348
            prefcapXperiod2 |  -.0194261   .0285878    -0.68   0.497    -.0757254    .0368732
             prefcapXperiod |   .0099247   .0236393     0.42   0.675    -.0366293    .0564786
           lnjinshiXperiod1 |   .0111132    .015305     0.73   0.468    -.0190276     .041254
           lnjinshiXperiod2 |   .0114418   .0158033     0.72   0.470    -.0196803    .0425639
            lnjinshiXperiod |  -.0189036   .0111261    -1.70   0.091    -.0408148    .0030076
       lncntyquota0Xperiod1 |  -.0241371   .0158135    -1.53   0.128    -.0552794    .0070051
       lncntyquota0Xperiod2 |  -.0270572   .0174301    -1.55   0.122    -.0613831    .0072686
        lncntyquota0Xperiod |   .0244165   .0114248     2.14   0.034      .001917     .046916
          lncntypopXperiod1 |   .0206198   .0135621     1.52   0.130    -.0060887    .0473283
          lncntypopXperiod2 |    .022698   .0146612     1.55   0.123     -.006175    .0515709
           lncntypopXperiod |  -.0173109   .0082424    -2.10   0.037    -.0335429   -.0010788
         lncntyareaXperiod1 |  -.0075081   .0123571    -0.61   0.544    -.0318435    .0168273
         lncntyareaXperiod2 |   -.001805   .0116668    -0.15   0.877     -.024781     .021171
          lncntyareaXperiod |  -.0069958   .0080965    -0.86   0.388    -.0229406    .0089491
            mainrivXperiod1 |    .004465   .0171628     0.26   0.795    -.0293346    .0382646
            mainrivXperiod2 |   .0030546   .0188724     0.16   0.872    -.0341117    .0402208
             mainrivXperiod |  -.0188598   .0125042    -1.51   0.133    -.0434849    .0057653
         dist2canalXperiod1 |  -.0121339   .0044615    -2.72   0.007    -.0209203   -.0033476
         dist2canalXperiod2 |  -.0104499   .0054209    -1.93   0.055    -.0211256    .0002258
          dist2canalXperiod |    .008628   .0040491     2.13   0.034     .0006539    .0166022
             lnriceXperiod1 |     .05775   .0218283     2.65   0.009     .0147625    .1007376
             lnriceXperiod2 |   .0362981   .0292282     1.24   0.215    -.0212624    .0938586
              lnriceXperiod |  -.0412495   .0174977    -2.36   0.019    -.0757086   -.0067904
            lnwheatXperiod1 |   .0530992   .0197651     2.69   0.008     .0141748    .0920236
            lnwheatXperiod2 |   .0146594   .0237027     0.62   0.537    -.0320193    .0613382
             lnwheatXperiod |  -.0485985   .0186828    -2.60   0.010    -.0853914   -.0118055
       dist_nanjingXperiod1 |   .0186685   .0051727     3.61   0.000     .0084817    .0288553
       dist_nanjingXperiod2 |   .0155052   .0069412     2.23   0.026     .0018356    .0291747
        dist_nanjingXperiod |  -.0119494   .0046464    -2.57   0.011    -.0210998   -.0027991
     Taiping_route1Xperiod1 |  -.0655647   .0424863    -1.54   0.124     -.149235    .0181056
     Taiping_route1Xperiod2 |  -.0884427   .0359157    -2.46   0.014    -.1591733   -.0177121
      Taiping_route1Xperiod |   .0284777   .0379696     0.75   0.454    -.0462977    .1032531
                      _cons |   .0245785   .0681667     0.36   0.719    -.1096653    .1588224
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |       111           0         111     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. parmest, saving( Results\ConnectionOnRentry_all0_yearly_0, replace)
file Results\ConnectionOnRentry_all0_yearly_0.dta saved

.  
. 
. 
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnSenior_all0_yearly_02, clear

.  
. 
. gen i=_n

. keep if i<=91
(214 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30    .0362093    .0171097          0   .0662229

. gen bench=r(mean)

. 
.  
. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) ) (line zero time , lp(dash) lw(medthick) lc(g
> s0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("A. All officials", size(medium) )  xlabel(1820(30)1910) ylabel(-0.2
> (0.2)0.4)   legend(order(1 "95% CI" 2 "Effects of connections * Hunan" ) row(2) region(color(none)))  graphre
> gion(color(white) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCnty_hXconn_all0_yearly_A_0
> .gph", replace) xsize(3) ysize(3)
(file Results\AllCnty_hXconn_all0_yearly_A_0.gph saved)

.  
. restore 

. 
. 
. *********************************** New  entry
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnEntry_all0_yearly_0, clear

. 
. 
. gen i=_n

. keep if i<=91
(124 observations deleted)

. gen time=i

. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30    .0506629    .0135066          0   .0651905

. gen bench=r(mean)

. 
.  
. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) ) (line zero time , lp(dash) lw(medthick) lc(g
> s0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("B. Newly promoted", size(medium) )  xlabel(1820(30)1910) ylabel(-0.
> 2(0.2)0.4)   legend(order(1 "95% CI" 2 "Effects of connections * Hunan" ) row(2) region(color(none)))  graphr
> egion(color(white) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCnty_hXconn_all0_yearly_En
> try_0.gph", replace) xsize(3) ysize(3)
(file Results\AllCnty_hXconn_all0_yearly_Entry_0.gph saved)

.  
. restore 

. 
. 
. 
. 
. *********************************** RE  entry
. 
. preserve

. 
. **************
.  
. use Results\ConnectionOnRentry_all0_yearly_0, clear

. 
. 
. gen i=_n

. keep if i<=91
(124 observations deleted)

. gen time=i

. 
. 
. gen zero=0

. label var zero "the effect = 0"

. label var estimate "the coefficent of logged total deaths"

. 
. keep time max95 min95 time estimate zero

. 
. 
. replace time=1820+time
(91 real changes made)

. replace time=1820 if time==1911
(1 real change made)

.  
. 
. foreach x of varlist estimate   {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)

. 
. foreach x of varlist   max95 min95  {
  2. replace `x'=0 if  time==1820
  3. }
(1 real change made)
(1 real change made)

. 
. 
. gen year=time 

. 
. ****
. sort time

. label var time "Year"

. 
.  
. sum estimate if year<=1849

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    estimate |         30   -.0136748    .0147043  -.0367631   .0150501

. gen bench=r(mean)

. 
.  
. ***** 
. twoway  (rarea max95 min95 time, lstyle(ci)  msize(tiny) fc(gs12) c(none) lc(gs12))   (connect estimate time,
>  msize(vsmall) lp(solid) lw(medthick) lc(gs7)  xline(1850, lpattern(dash) lcolor(blue))  xline(1853, lpattern
> (solid) lcolor(blue)) xline(1864, lpattern(dash) lcolor(blue)) ) (line zero time , lp(dash) lw(medthick) lc(g
> s0) )   /// 
> , leg(region(lp(blank)) rows(3))   title("C. Re-promoted", size(medium) )  xlabel(1820(30)1910) ylabel(-0.2(0
> .2)0.4)   legend(order(1 "95% CI" 2 "Effects of connections * Hunan" ) row(2) region(color(none)))  graphregi
> on(color(white) ifcolor(white) ilcolor(white) fcolor(white)) saving("Results\AllCnty_hXconn_all0_yearly_Rentr
> y_0.gph", replace) xsize(3) ysize(3)
(file Results\AllCnty_hXconn_all0_yearly_Rentry_0.gph saved)

.  
. restore 

. 
. 
. 
. 
. 
. *********************************************************************************************************
. ************************************************** Yearly effect of connections all, newly promoted, and re-p
> romoted 
. ************************************************** 
. 
. graph combine  Results\AllCnty_hXconn_all0_yearly_A_0.gph  Results\AllCnty_hXconn_all0_yearly_Entry_0.gph Res
> ults\AllCnty_hXconn_all0_yearly_Rentry_0.gph, row(1) xsize(9) ysize(4.5) graphregion(color(white) ifcolor(whi
> te) ilcolor(white) fcolor(white))

. 
. graph export Results\Figure_C1.png, replace
(file Results\Figure_C1.png written in PNG format)

. 
. 
. 
end of do-file

. 
. 
. ******** Table C.2. The Impact of Elite Networks on Elite Power: Inside and Outside the Network
. 
. 
. do Programs\Appendix_Table_C2.do

. 
. *********************************************************************************
. * Table C.2. The Impact of Elite Networks on Elite Power: Inside and Outside the Network
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen nhXZenghu_all_invdist=nonhunan*Zenghu_all_invdist

. gen hXZenghu_all_invdist=hunan*Zenghu_all_invdist

. 
. gen nhXZeng_all0_invdist=nonhunan*Zeng_all0_invdist

. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. foreach x of varlist hunan  Zenghu_all_invdist   Zeng_all0_invdist Zeng_all0_invdist_pc  invdist0_L1 invdist0
> _F1    Zeng_exam0_invdist  Zeng_Extraexam_invdist   Zeng_BMF_invdist    Zeng_juren0_invdist  nhXZenghu_all_in
> vdist nhXZeng_all0_invdist  hXZenghu_all_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea mainriv dist2canal lnrice
>  lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea mainriv dist2canal lnrice
>  lnwheat dist_nanjing  Taiping_route1 {
  2. gen h`x'Xperiod=hunan*`x'*period
  3. }

. 
. 
. 
. ********************************************************************************
. 
. keep if year>=1820
(32,920 observations deleted)

. 
. foreach x of varlist alloff ZengConnected NotZengConnected entry reentry {
  2. sum `x'
  3. gen `x'_byavg=`x'/r(mean)
  4. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      alloff |    149,786    .0931529    .5403086          0         15

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
ZengConnec~d |    149,786    .0176185    .2115361          0          9

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
NotZengCon~d |    149,786    .0755344    .4612964          0         14

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       entry |    149,786    .0448106    .3270701          0         12

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     reentry |    149,786    .0483423    .3667321          0         11

. 
. 
. 
. ********************************************************************************
. 
. 
. ********* 
. 
. 
. reghdfe alloff_byavg        lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0_
> invdistXperiod hunanXperiod, absorb(year samcntyid ) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      12.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3837
                                                  Adj R-squared   =     0.3764
                                                  Within R-sq.    =     0.0042
Number of clusters (prefid)  =        255         Root MSE        =     4.5805

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
              alloff_byavg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0906927   .0387773     2.34   0.020     .0143268    .1670586
            prefcapXperiod |    .072003   .3732034     0.19   0.847    -.6629642    .8069703
           lnjinshiXperiod |  -.2096069   .1185502    -1.77   0.078    -.4430735    .0238597
       lncntyquota0Xperiod |  -.1716348    .242728    -0.71   0.480    -.6496506     .306381
          lncntypopXperiod |   .2628603   .1816039     1.45   0.149    -.0947809    .6205014
         lncntyareaXperiod |  -.0837179   .1661155    -0.50   0.615     -.410857    .2434213
            mainrivXperiod |   -.144379   .2179521    -0.66   0.508    -.5736025    .2848445
         dist2canalXperiod |   .0537823   .0804348     0.67   0.504    -.1046217    .2121863
             lnriceXperiod |  -.1243501   .4712363    -0.26   0.792    -1.052378    .8036779
            lnwheatXperiod |  -.5828582     .34705    -1.68   0.094     -1.26632    .1006039
       dist_nanjingXperiod |   -.052271   .1042787    -0.50   0.617     -.257632      .15309
     Taiping_route1Xperiod |  -.6479491   .6725376    -0.96   0.336    -1.972409    .6765111
hXZeng_all0_invdistXperiod |   .5268197    .135916     3.88   0.000     .2591539    .7944855
  Zeng_all0_invdistXperiod |   .1173238   .1196547     0.98   0.328    -.1183178    .3529654
              hunanXperiod |   .8801791   .6792113     1.30   0.196    -.4574239    2.217782
                     _cons |   .1123101   1.278001     0.09   0.930    -2.404517    2.629137
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C2.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod hun
> anXperiod)  se  bdec(3) rdec(3) nocons replace 
Results\Appendix_Table_C2.doc
dir : seeout

. 
. 
.  
. *********
. reghdfe ZengConnected_byavg       lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng
> _all0_invdistXperiod hunanXperiod, absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =       3.25
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.2282
                                                  Adj R-squared   =     0.2191
                                                  Within R-sq.    =     0.0024
Number of clusters (prefid)  =        255         Root MSE        =    10.6100

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
       ZengConnected_byavg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0557726   .0608633     0.92   0.360    -.0640884    .1756337
            prefcapXperiod |   .7839907   .7002247     1.12   0.264    -.5949951    2.162977
           lnjinshiXperiod |  -.6701011   .3370117    -1.99   0.048    -1.333794    -.006408
       lncntyquota0Xperiod |   .2995133   .6016887     0.50   0.619    -.8854208    1.484447
          lncntypopXperiod |  -.4020465   .3889908    -1.03   0.302    -1.168105    .3640115
         lncntyareaXperiod |   .2278387   .2209135     1.03   0.303    -.2072167    .6628941
            mainrivXperiod |  -.3353013   .4698633    -0.71   0.476    -1.260626    .5900229
         dist2canalXperiod |  -.0131758   .1331681    -0.10   0.921    -.2754301    .2490785
             lnriceXperiod |   .5296618   .7621533     0.69   0.488    -.9712829    2.030607
            lnwheatXperiod |  -.7641556   .6921332    -1.10   0.271    -2.127206    .5988952
       dist_nanjingXperiod |  -.0320669    .181229    -0.18   0.860    -.3889698     .324836
     Taiping_route1Xperiod |   .7770397   1.349717     0.58   0.565    -1.881023    3.435102
hXZeng_all0_invdistXperiod |   .8468075   .2802859     3.02   0.003     .2948272    1.398788
  Zeng_all0_invdistXperiod |   .2207367    .183778     1.20   0.231    -.1411861    .5826594
              hunanXperiod |   .0787205   .7459888     0.11   0.916    -1.390391    1.547832
                     _cons |   3.283399   2.586779     1.27   0.205    -1.810869    8.377667
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C2.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod hun
> anXperiod)  se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C2.doc
dir : seeout

. 
.  
. *********
. 
. reghdfe NotZengConnected_byavg       lnurbanpopXperiod-Taiping_route1Xperiod   hXZeng_all0_invdistXperiod    
> Zeng_all0_invdistXperiod hunanXperiod, absorb(year samcntyid) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      17.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3269
                                                  Adj R-squared   =     0.3189
                                                  Within R-sq.    =     0.0033
Number of clusters (prefid)  =        255         Root MSE        =     5.0401

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
    NotZengConnected_byavg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0988378   .0450587     2.19   0.029     .0101017     .187574
            prefcapXperiod |  -.0940687   .4203294    -0.22   0.823    -.9218434     .733706
           lnjinshiXperiod |  -.1021963   .1556733    -0.66   0.512    -.4087711    .2043785
       lncntyquota0Xperiod |  -.2815304   .2654953    -1.06   0.290     -.804383    .2413221
          lncntypopXperiod |   .4179503   .2249923     1.86   0.064    -.0251377    .8610384
         lncntyareaXperiod |  -.1563887   .1855942    -0.84   0.400    -.5218881    .2091107
            mainrivXperiod |  -.0998462   .2375942    -0.42   0.675    -.5677518    .3680594
         dist2canalXperiod |   .0694003   .0771297     0.90   0.369    -.0824948    .2212954
             lnriceXperiod |  -.2768989   .4488499    -0.62   0.538     -1.16084    .6070426
            lnwheatXperiod |  -.5405705   .3712616    -1.46   0.147    -1.271714    .1905727
       dist_nanjingXperiod |  -.0569836   .1005887    -0.57   0.572    -.2550778    .1411106
     Taiping_route1Xperiod |   -.980329   .6556104    -1.50   0.136    -2.271454    .3107958
hXZeng_all0_invdistXperiod |   .4521823   .1349798     3.35   0.001       .18636    .7180045
  Zeng_all0_invdistXperiod |   .0932027   .1253042     0.74   0.458    -.1535648    .3399702
              hunanXperiod |    1.06712   .7045427     1.51   0.131    -.3203693    2.454609
                     _cons |  -.6273491   1.582386    -0.40   0.692    -3.743617    2.488919
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C2.doc, keep( hXZeng_all0_invdistXperiod    Zeng_all0_invdistXperiod hun
> anXperiod)  se  bdec(3) rdec(3) nocons append
Results\Appendix_Table_C2.doc
dir : seeout

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. 
. ******** Table C.3. The Impact of Elite Networks on Exam Quotas and Numbers of Jinshi
. 
. 
. do Programs\Appendix_Table_C3.do

. 
. *********************************************************************************
. **** Table C.3. The Impact of Elite Networks on Exam Quotas and Numbers of Jinshi
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. foreach x of varlist hunan  Zeng_all0_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. ********************************************************************************
. 
. keep if year > =1820
(32,920 observations deleted)

. 
. 
. 
. *******
.  
. reghdfe lncntyquota_panel    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0
> _invdistXperiod hunanXperiod if year==1840|year==1880 , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      3,292
Absorbing 2 HDFE groups                           F(  15,    254) =     111.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9956
                                                  Adj R-squared   =     0.9912
                                                  Within R-sq.    =     0.4634
Number of clusters (prefid)  =        255         Root MSE        =     0.0812

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
         lncntyquota_panel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0080063   .0019833     4.04   0.000     .0041005    .0119122
            prefcapXperiod |   -.029051   .0075156    -3.87   0.000    -.0438517   -.0142502
           lnjinshiXperiod |   .0025172   .0054168     0.46   0.643    -.0081504    .0131848
       lncntyquota0Xperiod |  -.0748937   .0171093    -4.38   0.000    -.1085878   -.0411996
          lncntypopXperiod |   .1035949   .0182929     5.66   0.000     .0675697      .13962
         lncntyareaXperiod |  -.0305084     .01583    -1.93   0.055    -.0616832    .0006664
            mainrivXperiod |  -.0020871   .0090266    -0.23   0.817    -.0198636    .0156894
         dist2canalXperiod |   .0241465    .003292     7.33   0.000     .0176633    .0306297
             lnriceXperiod |   .0906488   .0196427     4.61   0.000     .0519654    .1293321
            lnwheatXperiod |  -.0735054   .0182423    -4.03   0.000    -.1094308     -.03758
       dist_nanjingXperiod |     -.0255   .0045344    -5.62   0.000    -.0344299   -.0165701
     Taiping_route1Xperiod |  -.0226814    .034495    -0.66   0.511    -.0906141    .0452512
hXZeng_all0_invdistXperiod |   .0187994   .0047832     3.93   0.000     .0093797    .0282191
  Zeng_all0_invdistXperiod |   .0017579   .0019734     0.89   0.374    -.0021284    .0056443
              hunanXperiod |   .0145946   .0326769     0.45   0.656    -.0497575    .0789467
                     _cons |   2.155534   .0839316    25.68   0.000     1.990243    2.320824
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         2           0           2     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C3.doc, keep( hXZeng_all0_invdistXperiod  )  se  bdec(3) rdec(3) nocons 
> replace
Results\Appendix_Table_C3.doc
dir : seeout

. 
. 
. *******
. 
. reghdfe lnjinshi_panel    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0_in
> vdistXperiod hunanXperiod if year==1840|year==1880, absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =      3,292
Absorbing 2 HDFE groups                           F(  15,    254) =      67.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9315
                                                  Adj R-squared   =     0.8617
                                                  Within R-sq.    =     0.5038
Number of clusters (prefid)  =        255         Root MSE        =     0.4368

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
            lnjinshi_panel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0308576   .0074145     4.16   0.000     .0162558    .0454594
            prefcapXperiod |   .0488238    .050764     0.96   0.337    -.0511482    .1487959
           lnjinshiXperiod |  -.5251413   .0245319   -21.41   0.000    -.5734531   -.4768294
       lncntyquota0Xperiod |  -.2592949   .0508851    -5.10   0.000    -.3595053   -.1590844
          lncntypopXperiod |   .2268573   .0474186     4.78   0.000     .1334736     .320241
         lncntyareaXperiod |    .059473   .0316122     1.88   0.061    -.0027825    .1217284
            mainrivXperiod |   .0400089   .0414458     0.97   0.335    -.0416124    .1216301
         dist2canalXperiod |   .0016873   .0104726     0.16   0.872    -.0189369    .0223114
             lnriceXperiod |  -.0564029   .0682186    -0.83   0.409    -.1907489    .0779432
            lnwheatXperiod |   .0028083   .0584102     0.05   0.962    -.1122216    .1178382
       dist_nanjingXperiod |   .0104392    .012699     0.82   0.412    -.0145695    .0354479
     Taiping_route1Xperiod |   .2130799   .1233631     1.73   0.085    -.0298649    .4560247
hXZeng_all0_invdistXperiod |   .0542131   .0232547     2.33   0.021     .0084164    .1000097
  Zeng_all0_invdistXperiod |   .0600147   .0194593     3.08   0.002     .0216925    .0983369
              hunanXperiod |  -.0040819   .0727779    -0.06   0.955    -.1474069    .1392431
                     _cons |   .2014201   .2510718     0.80   0.423    -.2930275    .6958677
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         2           0           2     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C3.doc, keep( hXZeng_all0_invdistXperiod  )  se  bdec(3) rdec(3) nocons 
>  append  
Results\Appendix_Table_C3.doc
dir : seeout

. 
. 
. *******
. 
. reghdfe alloff   lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod    Zeng_all0_invdistXper
> iod hunanXperiod, absorb(year samcntyid ) cluster(prefid   )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      12.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3837
                                                  Adj R-squared   =     0.3764
                                                  Within R-sq.    =     0.0042
Number of clusters (prefid)  =        255         Root MSE        =     0.4267

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0084483   .0036122     2.34   0.020     .0013346     .015562
            prefcapXperiod |   .0067073    .034765     0.19   0.847     -.061757    .0751716
           lnjinshiXperiod |  -.0195255   .0110433    -1.77   0.078    -.0412736    .0022226
       lncntyquota0Xperiod |  -.0159883   .0226108    -0.71   0.480    -.0605168    .0285403
          lncntypopXperiod |   .0244862   .0169169     1.45   0.149    -.0088291    .0578015
         lncntyareaXperiod |  -.0077986   .0154741    -0.50   0.615    -.0382725    .0226754
            mainrivXperiod |  -.0134493   .0203029    -0.66   0.508    -.0534327    .0265341
         dist2canalXperiod |     .00501   .0074927     0.67   0.504    -.0097458    .0197658
             lnriceXperiod |  -.0115836    .043897    -0.26   0.792    -.0980321    .0748649
            lnwheatXperiod |  -.0542949   .0323287    -1.68   0.094    -.1179614    .0093715
       dist_nanjingXperiod |  -.0048692   .0097139    -0.50   0.617    -.0239992    .0142608
     Taiping_route1Xperiod |  -.0603583   .0626488    -0.96   0.336    -.1837356     .063019
hXZeng_all0_invdistXperiod |   .0490748    .012661     3.88   0.000     .0241409    .0740086
  Zeng_all0_invdistXperiod |   .0109291   .0111462     0.98   0.328    -.0110216    .0328798
              hunanXperiod |   .0819912   .0632705     1.30   0.196    -.0426104    .2065928
                     _cons |    .010462   .1190495     0.09   0.930    -.2239878    .2449118
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C3.doc, keep( hXZeng_all0_invdistXperiod  )  se  bdec(3) rdec(3) nocons 
>  append   
Results\Appendix_Table_C3.doc
dir : seeout

. 
. 
. *******
. 
. reghdfe alloff  lnjinshi_panel lncntyquota_panel    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invd
> istXperiod    Zeng_all0_invdistXperiod hunanXperiod  , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  17,    254) =      13.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3840
                                                  Adj R-squared   =     0.3767
                                                  Within R-sq.    =     0.0047
Number of clusters (prefid)  =        255         Root MSE        =     0.4266

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
            lnjinshi_panel |   .0312024   .0167611     1.86   0.064     -.001806    .0642109
         lncntyquota_panel |   .0363924   .0640612     0.57   0.570    -.0897663    .1625511
         lnurbanpopXperiod |   .0071941   .0033286     2.16   0.032     .0006389    .0137493
            prefcapXperiod |   .0062411   .0348197     0.18   0.858    -.0623309    .0748131
           lnjinshiXperiod |  -.0032314   .0170889    -0.19   0.850    -.0368855    .0304226
       lncntyquota0Xperiod |  -.0051721   .0216449    -0.24   0.811    -.0477984    .0374542
          lncntypopXperiod |   .0136376    .016019     0.85   0.395    -.0179093    .0451846
         lncntyareaXperiod |   -.008544    .016277    -0.52   0.600    -.0405991    .0235111
            mainrivXperiod |  -.0146217   .0202506    -0.72   0.471    -.0545023    .0252588
         dist2canalXperiod |   .0040786   .0076428     0.53   0.594    -.0109728      .01913
             lnriceXperiod |  -.0131226   .0435632    -0.30   0.763    -.0989137    .0726685
            lnwheatXperiod |  -.0517075   .0325621    -1.59   0.114    -.1158336    .0124185
       dist_nanjingXperiod |  -.0042669   .0098584    -0.43   0.666    -.0236816    .0151478
     Taiping_route1Xperiod |  -.0661815   .0630963    -1.05   0.295    -.1904401     .058077
hXZeng_all0_invdistXperiod |    .046699   .0133201     3.51   0.001      .020467     .072931
  Zeng_all0_invdistXperiod |   .0089925    .010771     0.83   0.405    -.0122193    .0302043
              hunanXperiod |   .0815875   .0614923     1.33   0.186    -.0395122    .2026871
                     _cons |   -.060021   .1942487    -0.31   0.758    -.4425642    .3225223
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C3.doc, keep( hXZeng_all0_invdistXperiod    lnjinshi_panel lncntyquota_p
> anel)  se  bdec(3) rdec(3) nocons append  
Results\Appendix_Table_C3.doc
dir : seeout

. 
. 
. *******
. 
. gen lncntyquotaXperiod=lncntyquota*period

. gen lnjinshi_aft50Xperiod=lnjinshi_aft50*period

. 
. reghdfe alloff  lnjinshi_aft50Xperiod lncntyquotaXperiod    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_a
> ll0_invdistXperiod    Zeng_all0_invdistXperiod hunanXperiod  , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  17,    254) =      11.60
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3839
                                                  Adj R-squared   =     0.3766
                                                  Within R-sq.    =     0.0046
Number of clusters (prefid)  =        255         Root MSE        =     0.4266

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
     lnjinshi_aft50Xperiod |   .0282667   .0164514     1.72   0.087    -.0041319    .0606652
        lncntyquotaXperiod |   .0354728   .0641743     0.55   0.581    -.0909087    .1618543
         lnurbanpopXperiod |   .0067035   .0032117     2.09   0.038     .0003785    .0130286
            prefcapXperiod |   .0061158    .034906     0.18   0.861    -.0626263    .0748579
           lnjinshiXperiod |  -.0322655   .0097141    -3.32   0.001    -.0513959    -.013135
       lncntyquota0Xperiod |   -.041253   .0681682    -0.61   0.546    -.1754998    .0929938
          lncntypopXperiod |   .0134466   .0159118     0.85   0.399    -.0178892    .0447824
         lncntyareaXperiod |  -.0080229   .0162231    -0.49   0.621    -.0399718    .0239259
            mainrivXperiod |  -.0144273   .0202724    -0.71   0.477    -.0543507     .025496
         dist2canalXperiod |    .003926   .0076205     0.52   0.607    -.0110814    .0189333
             lnriceXperiod |  -.0123743   .0435192    -0.28   0.776    -.0980786    .0733301
            lnwheatXperiod |  -.0526677   .0327345    -1.61   0.109    -.1171333    .0117979
       dist_nanjingXperiod |  -.0041411   .0098366    -0.42   0.674    -.0235128    .0152306
     Taiping_route1Xperiod |  -.0673143   .0633115    -1.06   0.289    -.1919967     .057368
hXZeng_all0_invdistXperiod |   .0463674   .0134966     3.44   0.001     .0197878     .072947
  Zeng_all0_invdistXperiod |   .0085605   .0107548     0.80   0.427    -.0126194    .0297404
              hunanXperiod |   .0841487   .0613879     1.37   0.172    -.0367455    .2050428
                     _cons |   .0809061    .107959     0.75   0.454    -.1317027    .2935149
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. outreg2 using Results\Appendix_Table_C3.doc, keep( hXZeng_all0_invdistXperiod    lnjinshi_aft50Xperiod lncnty
> quotaXperiod)  se  bdec(3) rdec(3) nocons append  
Results\Appendix_Table_C3.doc
dir : seeout

. 
. 
. 
. 
. 
end of do-file

. 
. 
. 
. ******** Figure 7. National-level Power Distribution and the Contribution of Elite Networks
. 
. 
. do Programs\Figure_7.do

. 
. ************************************************************************************************
. ************* Figure 7: National-level Power Distribution and the Contribution of Elite Networks
. ************************************************************************************************
. 
. 
. use Data\EG_Index.dta, clear

. 
. 
. **** Gen Hunan dummy 
. gen hunan=(provcd==11)

.  
.  
. **** Gen number of offices net of the effect of connections*Hunan 
. gen alloff_NoConn=alloff

. replace alloff_NoConn=alloff_NoConn-estimate*Zeng_all0_invdist if hunan==1
(6,607 real changes made, 3,367 to missing)

. 
. sort provcd year 

. collapse (sum) alloff alloff_NoConn labor, by(provcd year)

. 
. 
. sort year

. by year: egen tot_labor=sum(labor)

. gen x=labor/tot_labor 

.  
. gen x2=x^2

. sort year

. by year: egen sigmax2=sum(x2)

. 
. 
. 
. 
. local vars "alloff  alloff_NoConn"

. 
. foreach y of local vars {
  2. 
. sort year
  3. by year: egen t`y'=sum(`y')
  4. gen s`y'=`y'/t`y'
  5. 
. gen H`y'=1/t`y'
  6. 
. gen sminusx`y'=(s`y'-x)
  7. 
. gen sminusx2`y'=(s`y'-x)^2
  8. sort year
  9. by year: egen G`y'=sum(sminusx2`y')
 10. 
. gen gama`y'=(G`y'-(1-sigmax2)*H`y')/((1-sigmax2)*(1-H`y'))
 11. }

. 
. 
. 
. ****************** ****************** ****************** ****************** 
. 
. keep if provcd==10
(1,547 observations deleted)

. 
. 
. keep year gamaalloff  gamaalloff_NoConn 

. 
. gen hXconnRole=gamaalloff-gamaalloff_NoConn

. label var hXconnRole "The role of Hunan*connections"

. 
. 
. label var gamaalloff "EG Index"

. label var gamaalloff_NoConn "EG Index excluding the Hunan*connected officials"

. 
. 
. ****
. 
. 
. *************************** Figure 7. National-level Power Distribution and the Contribution of Elite Network
> s
. 
. 
. scatter  gamaalloff  year, ylabel(0(0.02)0.06)   xlabel(1820(30)1910)  ms(O) mc(gs4) msize(medium) || scatter
>  gamaalloff_NoConn   year, ylabel(0(0.02)0.06) xlabel(1820(30)1910) ms(Oh) mc(blue) msize(medium) mlwidth(med
> thick) xtitle(Year) ytitle(EG index) title(A. EG index)  graphregion(color(white) ifcolor(white) ilcolor(whit
> e) fcolor(white)) legend(order(1 "Real" 2 "Counterfactual: Net the effect of Hunan*connections") region(color
> (none)))  saving(Results\ProvEG.gph, replace)  xsize(4.5) ysize(4.5)
(file Results\ProvEG.gph saved)

. 
. ****
. 
. scatter hXconnRole  year,  legend(on) xlabel(1820(30)1910) ylabel(0(0.01)0.03)   ms(Oh) mc(gs1) mlwidth(medth
> ick) msize(medium)  xtitle(Year) ytitle(Difference in the two indices) title(B. The role of Hunan*connections
> ) graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(white)) legend(order(1 "The role of Hunan*con
> nections") region(color(none)))  saving(Results\Role_hXconn.gph, replace)   xsize(4.5) ysize(4.5) 
(file Results\Role_hXconn.gph saved)

. 
. graph combine Results\ProvEG.gph Results\Role_hXconn.gph,  row(1)  xsize(9) ysize(4.5) graphregion(color(whit
> e) ifcolor(white) ilcolor(white) fcolor(white))

. 
. graph export Results\Figure_7.png, replace 
(file Results\Figure_7.png written in PNG format)

. 
. 
end of do-file

. 
. 
. ******** Table C.4. The Impact of Elite Networks on Elite Power
. ******** Observation of Zeros in Our Data on National-level Offices
. 
. 
. do Programs\Appendix_Table_C4.do

. 
. *********************************************************************************
. ************************** Table C.4. The Impact of Elite Networks on Elite Power 
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. 
. 
. foreach x of varlist hunan  Zeng_all0_invdist hXZeng_all0_invdist{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. ********************************************************************************
. 
. keep if year > = 1820
(32,920 observations deleted)

. 
. 
. *********
. *********
. *********
. *********
. *********
. 
. reghdfe alloff    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod  Zeng_all0_invdistXperi
> od hunanXperiod   , absorb(year  samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =      12.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3837
                                                  Adj R-squared   =     0.3764
                                                  Within R-sq.    =     0.0042
Number of clusters (prefid)  =        255         Root MSE        =     0.4267

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0084483   .0036122     2.34   0.020     .0013346     .015562
            prefcapXperiod |   .0067073    .034765     0.19   0.847     -.061757    .0751716
           lnjinshiXperiod |  -.0195255   .0110433    -1.77   0.078    -.0412736    .0022226
       lncntyquota0Xperiod |  -.0159883   .0226108    -0.71   0.480    -.0605168    .0285403
          lncntypopXperiod |   .0244862   .0169169     1.45   0.149    -.0088291    .0578015
         lncntyareaXperiod |  -.0077986   .0154741    -0.50   0.615    -.0382725    .0226754
            mainrivXperiod |  -.0134493   .0203029    -0.66   0.508    -.0534327    .0265341
         dist2canalXperiod |     .00501   .0074927     0.67   0.504    -.0097458    .0197658
             lnriceXperiod |  -.0115836    .043897    -0.26   0.792    -.0980321    .0748649
            lnwheatXperiod |  -.0542949   .0323287    -1.68   0.094    -.1179614    .0093715
       dist_nanjingXperiod |  -.0048692   .0097139    -0.50   0.617    -.0239992    .0142608
     Taiping_route1Xperiod |  -.0603583   .0626488    -0.96   0.336    -.1837356     .063019
hXZeng_all0_invdistXperiod |   .0490748    .012661     3.88   0.000     .0241409    .0740086
  Zeng_all0_invdistXperiod |   .0109291   .0111462     0.98   0.328    -.0110216    .0328798
              hunanXperiod |   .0819912   .0632705     1.30   0.196    -.0426104    .2065928
                     _cons |    .010462   .1190495     0.09   0.930    -.2239878    .2449118
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C4.doc, keep( hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod )  s
> e  bdec(3) rdec(3) nocons  replace  
Results\Appendix_Table_C4.doc
dir : seeout

. 
. 
. 
. reghdfe alloffd    lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod   Zeng_all0_invdistXpe
> riod hunanXperiod   , absorb(year  samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 2 HDFE groups                           F(  15,    254) =       2.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0009
                                                  R-squared       =     0.3212
                                                  Adj R-squared   =     0.3132
                                                  Within R-sq.    =     0.0035
Number of clusters (prefid)  =        255         Root MSE        =     0.1787

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                   alloffd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .0026668   .0012909     2.07   0.040     .0001246     .005209
            prefcapXperiod |   .0084621   .0118284     0.72   0.475    -.0148322    .0317563
           lnjinshiXperiod |  -.0108326   .0049576    -2.19   0.030    -.0205958   -.0010695
       lncntyquota0Xperiod |  -.0074523   .0100594    -0.74   0.459    -.0272627    .0123582
          lncntypopXperiod |   .0136034   .0080196     1.70   0.091      -.00219    .0293968
         lncntyareaXperiod |  -.0037094   .0063497    -0.58   0.560    -.0162142    .0087954
            mainrivXperiod |  -.0107668   .0080642    -1.34   0.183    -.0266479    .0051144
         dist2canalXperiod |   .0022566   .0026002     0.87   0.386     -.002864    .0073772
             lnriceXperiod |    .001694   .0152953     0.11   0.912    -.0284278    .0318158
            lnwheatXperiod |  -.0099587    .013906    -0.72   0.475    -.0373443     .017427
       dist_nanjingXperiod |  -.0021805   .0033508    -0.65   0.516    -.0087794    .0044185
     Taiping_route1Xperiod |    .004243   .0306327     0.14   0.890    -.0560834    .0645693
hXZeng_all0_invdistXperiod |   .0076317   .0037495     2.04   0.043     .0002475    .0150158
  Zeng_all0_invdistXperiod |  -.0017113   .0020784    -0.82   0.411    -.0058044    .0023818
              hunanXperiod |   .0486887   .0262911     1.85   0.065    -.0030875     .100465
                     _cons |  -.0162321   .0598947    -0.27   0.787    -.1341856    .1017214
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C4.doc, keep( hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod )  s
> e  bdec(3) rdec(3) nocons  append  
Results\Appendix_Table_C4.doc
dir : seeout

. 
. 
. 
. reghdfe alloff   lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod Zeng_all0_invdistXperiod
>  hunanXperiod   if alloffd==1, absorb(year samcntyid  ) cluster(prefid )
(dropped 60 singleton observations)
(MWFE estimator converged in 11 iterations)

HDFE Linear regression                            Number of obs   =      7,263
Absorbing 2 HDFE groups                           F(  15,    171) =       6.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3987
                                                  Adj R-squared   =     0.3507
                                                  Within R-sq.    =     0.0200
Number of clusters (prefid)  =        172         Root MSE        =     1.2825

                                             (Std. Err. adjusted for 172 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
         lnurbanpopXperiod |   .1006275   .0826462     1.22   0.225    -.0625105    .2637656
            prefcapXperiod |  -.0781412   .2414373    -0.32   0.747    -.5547224      .39844
           lnjinshiXperiod |  -.0896887   .1334349    -0.67   0.502    -.3530805     .173703
       lncntyquota0Xperiod |   .0195512   .3417311     0.06   0.954    -.6550033    .6941058
          lncntypopXperiod |   .0340714   .2426359     0.14   0.888    -.4448759    .5130187
         lncntyareaXperiod |  -.1111634   .2354073    -0.47   0.637    -.5758418    .3535151
            mainrivXperiod |   .2574394   .1932058     1.33   0.184    -.1239361     .638815
         dist2canalXperiod |   .0028146   .0475733     0.06   0.953    -.0910919    .0967211
             lnriceXperiod |   .2080968   .4499141     0.46   0.644     -.680004    1.096198
            lnwheatXperiod |  -.6171851   .3177113    -1.94   0.054    -1.244326    .0099561
       dist_nanjingXperiod |   .0181135   .0534744     0.34   0.735    -.0874416    .1236685
     Taiping_route1Xperiod |    -.05399   .2693216    -0.20   0.841    -.5856131    .4776331
hXZeng_all0_invdistXperiod |   .7900295   .1639628     4.82   0.000     .4663778    1.113681
  Zeng_all0_invdistXperiod |   .0214583   .0162921     1.32   0.190    -.0107013    .0536178
              hunanXperiod |  -1.732263   .5588813    -3.10   0.002    -2.835458   -.6290687
                     _cons |   1.900072    1.60401     1.18   0.238    -1.266138    5.066282
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       432         432           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C4.doc, keep( hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod )  s
> e  bdec(3) rdec(3) nocons  append  
Results\Appendix_Table_C4.doc
dir : seeout

. 
. 
. 
. *********
. 
. 
. reghdfe alloff  lnurbanpop prefcap lnjinshi lncntyquota0  lncntypop lncntyarea mainriv dist2canal lnrice lnwh
> eat  dist_nanjing Taiping_route1   lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXperiod  Zeng_
> all0_invdist hXZeng_all0_invdist  Zeng_all0_invdistXperiod hunanXperiod  hunan, absorb(year   ) cluster(prefi
> d )
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =    149,786
Absorbing 1 HDFE group                            F(  30,    254) =      41.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1302
                                                  Adj R-squared   =     0.1295
                                                  Within R-sq.    =     0.1293
Number of clusters (prefid)  =        255         Root MSE        =     0.5041

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
                lnurbanpop |    .006818   .0035307     1.93   0.055    -.0001352    .0137711
                   prefcap |   -.029082   .0271774    -1.07   0.286    -.0826038    .0244398
                  lnjinshi |   .0909283   .0191963     4.74   0.000     .0531241    .1287326
              lncntyquota0 |  -.0573273   .0225024    -2.55   0.011    -.1016423   -.0130123
                 lncntypop |   .0244299   .0161162     1.52   0.131    -.0073086    .0561683
                lncntyarea |   .0224629   .0119354     1.88   0.061    -.0010421     .045968
                   mainriv |   .0243498   .0160096     1.52   0.130    -.0071786    .0558782
                dist2canal |   .0076609   .0046417     1.65   0.100    -.0014802    .0168019
                    lnrice |  -.0394902    .024224    -1.63   0.104    -.0871957    .0082153
                   lnwheat |  -.0393608   .0237494    -1.66   0.099    -.0861316    .0074101
              dist_nanjing |  -.0077985   .0056267    -1.39   0.167    -.0188794    .0032823
            Taiping_route1 |  -.0224777   .0430076    -0.52   0.602    -.1071745    .0622192
         lnurbanpopXperiod |   .0084483   .0036124     2.34   0.020     .0013342    .0155624
            prefcapXperiod |   .0067073   .0347667     0.19   0.847    -.0617605    .0751751
           lnjinshiXperiod |  -.0195255   .0110439    -1.77   0.078    -.0412747    .0022237
       lncntyquota0Xperiod |  -.0159883    .022612    -0.71   0.480    -.0605191    .0285425
          lncntypopXperiod |   .0244862   .0169178     1.45   0.149    -.0088308    .0578032
         lncntyareaXperiod |  -.0077986   .0154749    -0.50   0.615    -.0382741    .0226769
            mainrivXperiod |  -.0134493   .0203039    -0.66   0.508    -.0534347    .0265361
         dist2canalXperiod |     .00501   .0074931     0.67   0.504    -.0097466    .0197665
             lnriceXperiod |  -.0115836   .0438992    -0.26   0.792    -.0980364    .0748693
            lnwheatXperiod |  -.0542949   .0323303    -1.68   0.094    -.1179646    .0093747
       dist_nanjingXperiod |  -.0048692   .0097144    -0.50   0.617    -.0240001    .0142617
     Taiping_route1Xperiod |  -.0603583    .062652    -0.96   0.336    -.1837418    .0630251
hXZeng_all0_invdistXperiod |   .0490748   .0126616     3.88   0.000     .0241397    .0740099
         Zeng_all0_invdist |   .0589984   .0155525     3.79   0.000     .0283701    .0896266
       hXZeng_all0_invdist |  -.0654793   .0137482    -4.76   0.000    -.0925543   -.0384044
  Zeng_all0_invdistXperiod |   .0109291   .0111467     0.98   0.328    -.0110227    .0328809
              hunanXperiod |   .0819912   .0632737     1.30   0.196    -.0426166    .2065991
                     hunan |    .062692   .0241385     2.60   0.010     .0151549    .1102292
                     _cons |   -.405835   .2014528    -2.01   0.045    -.8025655   -.0091045
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
-----------------------------------------------------+

. 
. outreg2 using Results\Appendix_Table_C4.doc, keep( hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod )  s
> e  bdec(3) rdec(3) nocons  append  
Results\Appendix_Table_C4.doc
dir : seeout

. 
. 
. 
. *********
. xi: zinb alloff   i.year  lnurbanpop prefcap lnjinshi lncntyquota0  lncntypop lncntyarea mainriv dist2canal l
> nrice lnwheat  dist_nanjing Taiping_route1   lnurbanpopXperiod-Taiping_route1Xperiod  hXZeng_all0_invdistXper
> iod  Zeng_all0_invdist hXZeng_all0_invdist  Zeng_all0_invdistXperiod hunanXperiod  hunan, inflate( i.year  ln
> urbanpop prefcap lnjinshi lncntyquota0  lncntypop lncntyarea mainriv dist2canal lnrice lnwheat  dist_nanjing 
> Taiping_route1   lnurbanpopXperiod-Taiping_route1Xperiod   Zeng_all0_invdist hXZeng_all0_invdist   hunanXperi
> od  hunan)  probit  vce(cluster prefid)
i.year            _Iyear_1820-1910    (naturally coded; _Iyear_1820 omitted)

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -46291.607  (not concave)
Iteration 1:   log pseudolikelihood = -39881.063  (not concave)
Iteration 2:   log pseudolikelihood = -36477.557  (not concave)
Iteration 3:   log pseudolikelihood = -34800.949  (not concave)
Iteration 4:   log pseudolikelihood = -33810.369  
Iteration 5:   log pseudolikelihood = -31997.468  
Iteration 6:   log pseudolikelihood =   -31837.7  
Iteration 7:   log pseudolikelihood = -31835.756  
Iteration 8:   log pseudolikelihood = -31835.753  
Iteration 9:   log pseudolikelihood = -31835.753  

Fitting full model:

Iteration 0:   log pseudolikelihood = -31835.753  
Iteration 1:   log pseudolikelihood = -31361.462  (not concave)
Iteration 2:   log pseudolikelihood = -30786.812  
Iteration 3:   log pseudolikelihood = -30649.837  
Iteration 4:   log pseudolikelihood = -30635.416  
Iteration 5:   log pseudolikelihood = -30635.218  
Iteration 6:   log pseudolikelihood = -30635.217  

Zero-inflated negative binomial regression      Number of obs     =    149,786
                                                Nonzero obs       =      7,323
                                                Zero obs          =    142,463

Inflation model      = probit                   Wald chi2(120)    =          .
Log pseudolikelihood = -30635.22                Prob > chi2       =          .

                                             (Std. Err. adjusted for 255 clusters in prefid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
alloff                     |
               _Iyear_1821 |  -.1452514   .1488516    -0.98   0.329     -.436995    .1464923
               _Iyear_1822 |   .1611879   .1447419     1.11   0.265     -.122501    .4448768
               _Iyear_1823 |  -.2415932   .1675707    -1.44   0.149    -.5700257    .0868393
               _Iyear_1824 |  -.3435535   .2108001    -1.63   0.103     -.756714    .0696071
               _Iyear_1825 |  -.3802312   .1865365    -2.04   0.042    -.7458361   -.0146263
               _Iyear_1826 |  -.4309108   .1821942    -2.37   0.018    -.7880048   -.0738167
               _Iyear_1827 |  -.4734419   .1744852    -2.71   0.007    -.8154266   -.1314572
               _Iyear_1828 |  -.3995581    .183277    -2.18   0.029    -.7587743   -.0403418
               _Iyear_1829 |  -.5156539   .1943431    -2.65   0.008    -.8965593   -.1347484
               _Iyear_1830 |  -.2405099   .1908617    -1.26   0.208    -.6145919    .1335721
               _Iyear_1831 |   .0653869   .1817183     0.36   0.719    -.2907745    .4215483
               _Iyear_1832 |   .1229016   .1913005     0.64   0.521    -.2520405    .4978438
               _Iyear_1833 |   .1567853   .2182473     0.72   0.473    -.2709717    .5845422
               _Iyear_1834 |  -.0374813   .2095621    -0.18   0.858    -.4482155    .3732529
               _Iyear_1835 |  -.0941319   .2144432    -0.44   0.661    -.5144328     .326169
               _Iyear_1836 |   .0470772   .2296907     0.20   0.838    -.4031084    .4972628
               _Iyear_1837 |  -.1224155   .2165651    -0.57   0.572    -.5468754    .3020443
               _Iyear_1838 |  -.0965064   .2511445    -0.38   0.701    -.5887405    .3957277
               _Iyear_1839 |   .0539863   .2176977     0.25   0.804    -.3726934     .480666
               _Iyear_1840 |   .0933776   .2220708     0.42   0.674    -.3418732    .5286283
               _Iyear_1841 |  -.0780167   .2301673    -0.34   0.735    -.5291364    .3731029
               _Iyear_1842 |  -.4335511   .2369857    -1.83   0.067    -.8980345    .0309324
               _Iyear_1843 |   .0225942   .1888109     0.12   0.905    -.3474685    .3926568
               _Iyear_1844 |   -.077141   .2018004    -0.38   0.702    -.4726625    .3183805
               _Iyear_1845 |  -.0080114    .228764    -0.04   0.972    -.4563807    .4403579
               _Iyear_1846 |  -.0322094   .2082625    -0.15   0.877    -.4403964    .3759776
               _Iyear_1847 |  -.0253235   .2203183    -0.11   0.908    -.4571394    .4064925
               _Iyear_1848 |  -.1957965    .220986    -0.89   0.376    -.6289212    .2373282
               _Iyear_1849 |   .1562184   .2109325     0.74   0.459    -.2572016    .5696384
               _Iyear_1850 |   .0036572   .2479105     0.01   0.988    -.4822385    .4895528
               _Iyear_1851 |  -.3021967   .2079732    -1.45   0.146    -.7098168    .1054233
               _Iyear_1852 |   .1371394   .2404623     0.57   0.568     -.334158    .6084369
               _Iyear_1853 |  -.1490235   .2387556    -0.62   0.533    -.6169758    .3189289
               _Iyear_1854 |   2.293325   3.891257     0.59   0.556    -5.333398    9.920047
               _Iyear_1855 |   1.879459   3.877097     0.48   0.628    -5.719512    9.478431
               _Iyear_1856 |   2.085221   3.875618     0.54   0.591    -5.510851    9.681294
               _Iyear_1857 |   1.731624   3.888575     0.45   0.656    -5.889843    9.353091
               _Iyear_1858 |    2.03968   3.885638     0.52   0.600     -5.57603     9.65539
               _Iyear_1859 |   2.079861   3.901021     0.53   0.594       -5.566    9.725723
               _Iyear_1860 |    2.21025   3.900257     0.57   0.571    -5.434114    9.854614
               _Iyear_1861 |   2.128924   3.857793     0.55   0.581    -5.432212     9.69006
               _Iyear_1862 |   2.448688   3.874434     0.63   0.527    -5.145062    10.04244
               _Iyear_1863 |   2.467064   3.905404     0.63   0.528    -5.187388    10.12152
               _Iyear_1864 |    2.27611   3.890744     0.59   0.559    -5.349607    9.901828
               _Iyear_1865 |   2.250021   3.875723     0.58   0.562    -5.346257    9.846299
               _Iyear_1866 |   2.316405   3.882814     0.60   0.551    -5.293771    9.926581
               _Iyear_1867 |   2.484515   3.870704     0.64   0.521    -5.101925    10.07096
               _Iyear_1868 |   2.337398   3.888342     0.60   0.548    -5.283612    9.958409
               _Iyear_1869 |   2.088611   3.866229     0.54   0.589    -5.489059    9.666281
               _Iyear_1870 |    2.15511    3.82485     0.56   0.573    -5.341458    9.651677
               _Iyear_1871 |   2.184834    3.86499     0.57   0.572    -5.390406    9.760075
               _Iyear_1872 |   1.901101   3.859329     0.49   0.622    -5.663045    9.465247
               _Iyear_1873 |   1.989932   3.855892     0.52   0.606    -5.567476    9.547341
               _Iyear_1874 |   1.987637   3.875893     0.51   0.608    -5.608973    9.584246
               _Iyear_1875 |   2.204398   3.849085     0.57   0.567     -5.33967    9.748466
               _Iyear_1876 |   2.322304   3.828218     0.61   0.544    -5.180865    9.825474
               _Iyear_1877 |   2.208431   3.868536     0.57   0.568    -5.373761    9.790623
               _Iyear_1878 |     2.2277   3.850451     0.58   0.563    -5.319046    9.774446
               _Iyear_1879 |   2.476088   3.867803     0.64   0.522    -5.104666    10.05684
               _Iyear_1880 |   1.999881   3.819309     0.52   0.601    -5.485827    9.485589
               _Iyear_1881 |   2.503815    3.88569     0.64   0.519    -5.111996    10.11963
               _Iyear_1882 |     2.4229   3.881132     0.62   0.532    -5.183978    10.02978
               _Iyear_1883 |   2.478473   3.882307     0.64   0.523    -5.130708    10.08765
               _Iyear_1884 |   2.549358   3.870894     0.66   0.510    -5.037454    10.13617
               _Iyear_1885 |   2.587328   3.865846     0.67   0.503    -4.989591    10.16425
               _Iyear_1886 |   2.563121   3.861904     0.66   0.507    -5.006072    10.13231
               _Iyear_1887 |   2.309051   3.870835     0.60   0.551    -5.277647    9.895748
               _Iyear_1888 |   2.449423   3.859446     0.63   0.526    -5.114953     10.0138
               _Iyear_1889 |   2.547174   3.865015     0.66   0.510    -5.028116    10.12247
               _Iyear_1890 |   2.473625   3.856736     0.64   0.521    -5.085438    10.03269
               _Iyear_1891 |    2.31498   3.856627     0.60   0.548    -5.243869    9.873829
               _Iyear_1892 |   2.414334   3.876456     0.62   0.533     -5.18338    10.01205
               _Iyear_1893 |   2.274386   3.861876     0.59   0.556    -5.294751    9.843523
               _Iyear_1894 |   2.460477   3.866295     0.64   0.525    -5.117323    10.03828
               _Iyear_1895 |   2.643938   3.874107     0.68   0.495    -4.949172    10.23705
               _Iyear_1896 |   2.160261    3.86949     0.56   0.577      -5.4238    9.744322
               _Iyear_1897 |    2.20872   3.887953     0.57   0.570    -5.411527    9.828967
               _Iyear_1898 |   2.541578   3.910722     0.65   0.516    -5.123297    10.20645
               _Iyear_1899 |   2.059398   3.886356     0.53   0.596    -5.557719    9.676516
               _Iyear_1900 |   2.475177    3.89397     0.64   0.525    -5.156863    10.10722
               _Iyear_1901 |   2.385289   3.889908     0.61   0.540    -5.238791    10.00937
               _Iyear_1902 |   2.323609   3.869437     0.60   0.548    -5.260349    9.907567
               _Iyear_1903 |   2.309236   3.870205     0.60   0.551    -5.276227    9.894699
               _Iyear_1904 |   2.102362   3.885341     0.54   0.588    -5.512766    9.717491
               _Iyear_1905 |   2.199516   3.874489     0.57   0.570    -5.394343    9.793375
               _Iyear_1906 |   2.777236    3.87539     0.72   0.474    -4.818389    10.37286
               _Iyear_1907 |   2.511817   3.850431     0.65   0.514     -5.03489    10.05852
               _Iyear_1908 |   2.414121   3.836113     0.63   0.529    -5.104523    9.932765
               _Iyear_1909 |   2.272534   3.821242     0.59   0.552    -5.216963    9.762032
               _Iyear_1910 |   2.315065   3.825117     0.61   0.545    -5.182025    9.812156
                lnurbanpop |   .1535034    .046166     3.33   0.001     .0630197    .2439872
                   prefcap |  -.2513357   .1995345    -1.26   0.208    -.6424162    .1397448
                  lnjinshi |   .2944852   .0895827     3.29   0.001     .1189063    .4700641
              lncntyquota0 |  -.1583464   .2738295    -0.58   0.563    -.6950423    .3783495
                 lncntypop |   .2778882   .2144485     1.30   0.195    -.1424231    .6981994
                lncntyarea |  -.0598767    .160643    -0.37   0.709    -.3747312    .2549777
                   mainriv |  -.2766639   .1695716    -1.63   0.103     -.609018    .0556903
                dist2canal |   .0041361   .0364824     0.11   0.910     -.067368    .0756403
                    lnrice |  -.4882107   .2950257    -1.65   0.098     -1.06645    .0900291
                   lnwheat |   .4703372   .3125916     1.50   0.132     -.142331    1.083005
              dist_nanjing |  -.0363778   .0493206    -0.74   0.461    -.1330444    .0602888
            Taiping_route1 |  -.1513504   .2419426    -0.63   0.532    -.6255492    .3228484
         lnurbanpopXperiod |   .0932412   .0569609     1.64   0.102    -.0184001    .2048826
            prefcapXperiod |  -.1212085   .2506226    -0.48   0.629    -.6124198    .3700027
           lnjinshiXperiod |  -.1057822   .1267004    -0.83   0.404    -.3541104     .142546
       lncntyquota0Xperiod |  -.2263247   .4293521    -0.53   0.598    -1.067839      .61519
          lncntypopXperiod |  -.1665661   .3619673    -0.46   0.645    -.8760089    .5428766
         lncntyareaXperiod |   .1356629   .2538152     0.53   0.593    -.3618057    .6331315
            mainrivXperiod |    .189838   .2481027     0.77   0.444    -.2964344    .6761103
         dist2canalXperiod |    .021382   .0729976     0.29   0.770    -.1216907    .1644548
             lnriceXperiod |   .1466429   .5402878     0.27   0.786    -.9123018    1.205588
            lnwheatXperiod |  -.8908905   .4331232    -2.06   0.040    -1.739796   -.0419846
       dist_nanjingXperiod |  -.0287508   .0867826    -0.33   0.740    -.1988416      .14134
     Taiping_route1Xperiod |  -.3535688   .3798451    -0.93   0.352    -1.098052    .3909139
hXZeng_all0_invdistXperiod |   .2860718   .0677931     4.22   0.000     .1531998    .4189438
         Zeng_all0_invdist |   .0174693   .0096273     1.81   0.070    -.0013998    .0363384
       hXZeng_all0_invdist |  -.2958208   .0421979    -7.01   0.000    -.3785272   -.2131144
  Zeng_all0_invdistXperiod |  -.0002276   .0097216    -0.02   0.981    -.0192817    .0188264
              hunanXperiod |  -.8894302   .4643365    -1.92   0.055    -1.799513    .0206527
                     hunan |   1.142611    .326494     3.50   0.000     .5026948    1.782528
                     _cons |   -4.89007   2.104651    -2.32   0.020     -9.01511   -.7650304
---------------------------+----------------------------------------------------------------
inflate                    |
               _Iyear_1821 |  -.2956861    .097633    -3.03   0.002    -.4870433   -.1043289
               _Iyear_1822 |  -.1733187   .1047818    -1.65   0.098    -.3786872    .0320497
               _Iyear_1823 |  -.1995017   .1216631    -1.64   0.101    -.4379571    .0389536
               _Iyear_1824 |   -.246972   .1329308    -1.86   0.063    -.5075116    .0135676
               _Iyear_1825 |  -.4589674   .1505396    -3.05   0.002    -.7540197   -.1639151
               _Iyear_1826 |  -.3531813   .1483743    -2.38   0.017    -.6439895    -.062373
               _Iyear_1827 |  -.3276299   .1672276    -1.96   0.050    -.6553901    .0001302
               _Iyear_1828 |  -.3916981   .1635382    -2.40   0.017    -.7122271    -.071169
               _Iyear_1829 |  -.2609915   .1632047    -1.60   0.110    -.5808668    .0588838
               _Iyear_1830 |  -.1825174   .1565107    -1.17   0.244    -.4892727    .1242378
               _Iyear_1831 |  -.1725876   .1466734    -1.18   0.239    -.4600622     .114887
               _Iyear_1832 |    -.15689   .1405463    -1.12   0.264    -.4323557    .1185758
               _Iyear_1833 |  -.0501338    .156331    -0.32   0.748     -.356537    .2562694
               _Iyear_1834 |  -.1492139   .1653834    -0.90   0.367    -.4733595    .1749316
               _Iyear_1835 |  -.3029251   .1552608    -1.95   0.051    -.6072308    .0013805
               _Iyear_1836 |  -.0556971   .1528394    -0.36   0.716    -.3552568    .2438627
               _Iyear_1837 |  -.3089127   .1521842    -2.03   0.042    -.6071882   -.0106372
               _Iyear_1838 |  -.2110573   .1598399    -1.32   0.187    -.5243377    .1022231
               _Iyear_1839 |  -.2071466   .1466767    -1.41   0.158    -.4946276    .0803344
               _Iyear_1840 |  -.2579202    .148283    -1.74   0.082    -.5485496    .0327092
               _Iyear_1841 |  -.2143418    .154938    -1.38   0.167    -.5180146     .089331
               _Iyear_1842 |  -.4457718   .1791914    -2.49   0.013    -.7969805    -.094563
               _Iyear_1843 |  -.2681003    .149943    -1.79   0.074    -.5619833    .0257826
               _Iyear_1844 |  -.2394022   .1701169    -1.41   0.159    -.5728252    .0940209
               _Iyear_1845 |  -.0503139   .1663006    -0.30   0.762    -.3762571    .2756294
               _Iyear_1846 |  -.1926678   .1535186    -1.26   0.209    -.4935588    .1082232
               _Iyear_1847 |   -.058182   .1661368    -0.35   0.726    -.3838041      .26744
               _Iyear_1848 |  -.2147864   .1674264    -1.28   0.200    -.5429361    .1133633
               _Iyear_1849 |  -.1281836   .1578081    -0.81   0.417    -.4374819    .1811146
               _Iyear_1850 |  -.0072006    .169995    -0.04   0.966    -.3403847    .3259836
               _Iyear_1851 |  -.3668367   .1632085    -2.25   0.025    -.6867195   -.0469538
               _Iyear_1852 |  -.1148193   .1592566    -0.72   0.471    -.4269566    .1973179
               _Iyear_1853 |  -.2945138   .1884819    -1.56   0.118    -.6639316     .074904
               _Iyear_1854 |   3.704205   2.725395     1.36   0.174    -1.637472    9.045882
               _Iyear_1855 |   3.388465   2.697663     1.26   0.209    -1.898857    8.675787
               _Iyear_1856 |   3.698519   2.712431     1.36   0.173    -1.617747    9.014785
               _Iyear_1857 |   3.506508   2.703387     1.30   0.195    -1.792034    8.805049
               _Iyear_1858 |   3.612675   2.715514     1.33   0.183    -1.709635    8.934984
               _Iyear_1859 |    3.52494   2.711297     1.30   0.194    -1.789105    8.838985
               _Iyear_1860 |   3.561705   2.702972     1.32   0.188    -1.736023    8.859433
               _Iyear_1861 |   3.473466   2.696264     1.29   0.198    -1.811114    8.758046
               _Iyear_1862 |   3.621805   2.707795     1.34   0.181    -1.685376    8.928985
               _Iyear_1863 |   3.641817   2.718457     1.34   0.180     -1.68626    8.969894
               _Iyear_1864 |   3.512111   2.715225     1.29   0.196    -1.809633    8.833855
               _Iyear_1865 |   3.578827   2.715023     1.32   0.187    -1.742521    8.900174
               _Iyear_1866 |   3.554083   2.714517     1.31   0.190    -1.766272    8.874438
               _Iyear_1867 |   3.593501   2.706591     1.33   0.184    -1.711319    8.898321
               _Iyear_1868 |   3.685595   2.718863     1.36   0.175    -1.643279     9.01447
               _Iyear_1869 |   3.553077   2.701961     1.31   0.189    -1.742669    8.848824
               _Iyear_1870 |    3.43516   2.680617     1.28   0.200    -1.818753    8.689073
               _Iyear_1871 |   3.663303   2.686858     1.36   0.173    -1.602842    8.929448
               _Iyear_1872 |   3.453599    2.68443     1.29   0.198    -1.807787    8.714984
               _Iyear_1873 |   3.381776   2.689878     1.26   0.209    -1.890287     8.65384
               _Iyear_1874 |   3.549565   2.698648     1.32   0.188    -1.739687    8.838818
               _Iyear_1875 |   3.502651   2.697044     1.30   0.194    -1.783458    8.788759
               _Iyear_1876 |    3.57301   2.684746     1.33   0.183    -1.688995    8.835014
               _Iyear_1877 |   3.767791   2.692279     1.40   0.162     -1.50898    9.044561
               _Iyear_1878 |   3.732163   2.698012     1.38   0.167    -1.555844    9.020169
               _Iyear_1879 |   3.701152   2.707325     1.37   0.172    -1.605108    9.007412
               _Iyear_1880 |    3.55096    2.68684     1.32   0.186     -1.71515    8.817071
               _Iyear_1881 |   3.745482     2.7085     1.38   0.167    -1.563081    9.054045
               _Iyear_1882 |   3.571506    2.70723     1.32   0.187    -1.734567    8.877579
               _Iyear_1883 |    3.70064    2.70986     1.37   0.172    -1.610587    9.011867
               _Iyear_1884 |   3.785722   2.722038     1.39   0.164    -1.549375    9.120819
               _Iyear_1885 |   3.594026   2.720414     1.32   0.186    -1.737887     8.92594
               _Iyear_1886 |   3.798053    2.72398     1.39   0.163    -1.540849    9.136955
               _Iyear_1887 |   3.727701   2.718222     1.37   0.170    -1.599916    9.055318
               _Iyear_1888 |   3.652818   2.711579     1.35   0.178    -1.661779    8.967415
               _Iyear_1889 |   3.651792   2.714444     1.35   0.179    -1.668421    8.972004
               _Iyear_1890 |   3.764233   2.714192     1.39   0.165    -1.555486    9.083952
               _Iyear_1891 |   3.493516   2.708631     1.29   0.197    -1.815303    8.802336
               _Iyear_1892 |   3.777707   2.722256     1.39   0.165    -1.557817     9.11323
               _Iyear_1893 |   3.577343   2.718717     1.32   0.188    -1.751245    8.905931
               _Iyear_1894 |   3.555827    2.71124     1.31   0.190    -1.758106     8.86976
               _Iyear_1895 |   3.808721    2.72149     1.40   0.162    -1.525301    9.142744
               _Iyear_1896 |   3.674476   2.717283     1.35   0.176    -1.651301    9.000252
               _Iyear_1897 |   3.448052   2.718325     1.27   0.205    -1.879767    8.775871
               _Iyear_1898 |   3.758139   2.726448     1.38   0.168    -1.585601    9.101878
               _Iyear_1899 |   3.562122   2.709609     1.31   0.189    -1.748613    8.872858
               _Iyear_1900 |   3.572242   2.699793     1.32   0.186    -1.719255     8.86374
               _Iyear_1901 |   3.497454   2.692044     1.30   0.194    -1.778855    8.773763
               _Iyear_1902 |   3.560381   2.690137     1.32   0.186    -1.712191    8.832953
               _Iyear_1903 |   3.327881     2.6881     1.24   0.216    -1.940697    8.596459
               _Iyear_1904 |   3.519042   2.680751     1.31   0.189    -1.735133    8.773216
               _Iyear_1905 |   3.478467   2.686774     1.29   0.195    -1.787513    8.744448
               _Iyear_1906 |   3.586816    2.69279     1.33   0.183    -1.690954    8.864587
               _Iyear_1907 |   3.420179   2.684739     1.27   0.203    -1.841813     8.68217
               _Iyear_1908 |   3.340498   2.684354     1.24   0.213    -1.920739    8.601735
               _Iyear_1909 |   3.338926   2.681574     1.25   0.213    -1.916862    8.594714
               _Iyear_1910 |    3.35314   2.685314     1.25   0.212    -1.909978    8.616259
                lnurbanpop |  -.0068404   .0562307    -0.12   0.903    -.1170506    .1033698
                   prefcap |   .1068899   .1881701     0.57   0.570    -.2619167    .4756966
                  lnjinshi |  -.3224779   .0689812    -4.67   0.000    -.4576785   -.1872773
              lncntyquota0 |   .3862374   .1949877     1.98   0.048     .0040685    .7684063
                 lncntypop |  -.0434822   .1859789    -0.23   0.815    -.4079942    .3210298
                lncntyarea |  -.2507285   .1519484    -1.65   0.099     -.548542     .047085
                   mainriv |  -.3756414   .1351324    -2.78   0.005    -.6404959   -.1107868
                dist2canal |   .0350859    .031112     1.13   0.259    -.0258926    .0960644
                    lnrice |  -.2528271   .2286681    -1.11   0.269    -.7010084    .1953542
                   lnwheat |   .3292954   .1992547     1.65   0.098    -.0612366    .7198273
              dist_nanjing |  -.0373384    .037623    -0.99   0.321    -.1110782    .0364014
            Taiping_route1 |  -.0685729   .3057624    -0.22   0.823    -.6678562    .5307104
         lnurbanpopXperiod |  -.0608152   .0689382    -0.88   0.378    -.1959315    .0743012
            prefcapXperiod |  -.1400442     .22422    -0.62   0.532    -.5795073    .2994189
           lnjinshiXperiod |   .1872251    .084663     2.21   0.027     .0212886    .3531616
       lncntyquota0Xperiod |   -.046259   .2892213    -0.16   0.873    -.6131223    .5206042
          lncntypopXperiod |  -.4038408   .2512024    -1.61   0.108    -.8961884    .0885068
         lncntyareaXperiod |   .2204108   .1824986     1.21   0.227    -.1372799    .5781015
            mainrivXperiod |   .3328978   .1808357     1.84   0.066    -.0215337    .6873292
         dist2canalXperiod |  -.0456451   .0427928    -1.07   0.286    -.1295175    .0382273
             lnriceXperiod |   .0793743   .3511604     0.23   0.821    -.6088875    .7676361
            lnwheatXperiod |  -.2364145   .2878979    -0.82   0.412    -.8006841    .3278551
       dist_nanjingXperiod |   .0324808    .054315     0.60   0.550    -.0739747    .1389362
     Taiping_route1Xperiod |   -.340239   .6136893    -0.55   0.579    -1.543048    .8625699
         Zeng_all0_invdist |  -.3879248   .0522949    -7.42   0.000    -.4904209   -.2854288
       hXZeng_all0_invdist |  -.2401098   .3138693    -0.76   0.444    -.8552824    .3750628
              hunanXperiod |  -1.182812   .5739307    -2.06   0.039    -2.307695   -.0579284
                     hunan |   .5505725   .4945061     1.11   0.266    -.4186415    1.519787
                     _cons |   3.763688   1.756462     2.14   0.032     .3210857    7.206291
---------------------------+----------------------------------------------------------------
                  /lnalpha |  -.0171503   .2293658    -0.07   0.940     -.466699    .4323984
---------------------------+----------------------------------------------------------------
                     alpha |   .9829959   .2254657                      .6270688    1.540949
--------------------------------------------------------------------------------------------

. 
. margins , dydx(hXZeng_all0_invdistXperiod Zeng_all0_invdistXperiod)

Average marginal effects                        Number of obs     =    149,786
Model VCE    : Robust

Expression   : Predicted number of events, predict()
dy/dx w.r.t. : hXZeng_all0_invdistXperiod Zeng_all0_invdistXperiod

--------------------------------------------------------------------------------------------
                           |            Delta-method
                           |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
hXZeng_all0_invdistXperiod |    .026217   .0063479     4.13   0.000     .0137754    .0386587
  Zeng_all0_invdistXperiod |  -.0000209   .0008911    -0.02   0.981    -.0017673    .0017256
--------------------------------------------------------------------------------------------

. 
. outreg2 using Results\Appendix_Table_C4.doc, keep( hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod )  s
> e  bdec(3) rdec(3) nocons  append  
Results\Appendix_Table_C4.doc
dir : seeout

. 
. 
. 
end of do-file

. 
. 
. ******** Table C.5. The Impact of Elite Networks and Elite Power: Varying Comparison Provinces
. 
. 
. do Programs\Appendix_Table_C5.do

. *********************************************************************************
. *Table C5.The Impact of Elite Networks and Elite Power: Varying Comparison Provinces
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. gen hXZeng_all0_invdist=hunan*Zeng_all0_invdist

. gen hXZeng_exam0_invdist=hunan*Zeng_exam0_invdist

. gen hXZeng_Extraexam_invdist=hunan*Zeng_Extraexam_invdist

. 
. 
. foreach x of varlist hunan  Zeng_all0_invdist Zenghu_all_invdist Zeng_exam0_invdist Zeng_Extraexam_invdist hX
> Zeng_all0_invdist hXZeng_exam0_invdist hXZeng_Extraexam_invdist martyrs_tot_post{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. ********************************************************************************
. 
. keep if year > =1820
(32,920 observations deleted)

. 
. 
. ******************************* Hunan vs. Guangxi, Hubei， Jiangxi， Anhui and Jiangsu
. 
. reghdfe alloff   hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod  hunanXperiod   lnurbanpopXperiod-Taip
> ing_route1Xperiod   if provcd==11|provcd==5|provcd==16|provcd==10| provcd==3| provcd==2, absorb(year samcntyi
> d) cluster(prefid samcntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =     43,225
Absorbing 2 HDFE groups                           F(  15,     75) =      13.13
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3792
                                                  Adj R-squared   =     0.3708
Number of clusters (prefid)  =         76         Within R-sq.    =     0.0134
Number of clusters (samcntyid) =        475       Root MSE        =     0.5475

                                    (Std. Err. adjusted for 76 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
hXZeng_all0_invdistXperiod |    .080871   .0243099     3.33   0.001     .0324431    .1292989
  Zeng_all0_invdistXperiod |  -.0189949     .02098    -0.91   0.368    -.0607892    .0227994
              hunanXperiod |   .0108723   .0582663     0.19   0.852       -.1052    .1269446
         lnurbanpopXperiod |   .0182354   .0065598     2.78   0.007     .0051676    .0313032
            prefcapXperiod |  -.0230356   .0876682    -0.26   0.793    -.1976795    .1516084
           lnjinshiXperiod |  -.0339678   .0211295    -1.61   0.112    -.0760598    .0081243
       lncntyquota0Xperiod |  -.0525113   .0745202    -0.70   0.483    -.2009631    .0959405
          lncntypopXperiod |   .1722708   .0890979     1.93   0.057    -.0052212    .3497628
         lncntyareaXperiod |  -.0959439   .0577759    -1.66   0.101    -.2110393    .0191515
            mainrivXperiod |  -.1035889   .0613695    -1.69   0.096    -.2258431    .0186654
         dist2canalXperiod |   .0972643    .063865     1.52   0.132    -.0299613    .2244899
             lnriceXperiod |  -.3886368   .2081247    -1.87   0.066    -.8032424    .0259688
            lnwheatXperiod |   .1868829   .1368731     1.37   0.176    -.0857824    .4595481
       dist_nanjingXperiod |  -.0817991   .0647822    -1.26   0.211    -.2108518    .0472537
     Taiping_route1Xperiod |  -.0567355   .0694063    -0.82   0.416    -.1949999    .0815289
                     _cons |  -.5316255   .4851467    -1.10   0.277    -1.498087    .4348363
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       475         475           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(  hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod hun
> anXperiod )  se  bdec(3) rdec(3) nocons replace 
Results\Appendix_Table_C5.doc
dir : seeout

. 
. 
. reghdfe alloff  martyrs_tot_postXperiod   hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod  hunanXperiod
>  lnurbanpopXperiod-Taiping_route1Xperiod  if provcd==11|provcd==5|provcd==16|provcd==10| provcd==3| provcd==2
> , absorb(year samcntyid ) cluster(prefid samcntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =     43,225
Absorbing 2 HDFE groups                           F(  16,     75) =       9.48
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3965
                                                  Adj R-squared   =     0.3883
Number of clusters (prefid)  =         76         Within R-sq.    =     0.0410
Number of clusters (samcntyid) =        475       Root MSE        =     0.5398

                                    (Std. Err. adjusted for 76 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
   martyrs_tot_postXperiod |   .4141817   .1024539     4.04   0.000     .2100831    .6182803
hXZeng_all0_invdistXperiod |   .0223233   .0252618     0.88   0.380    -.0280009    .0726474
  Zeng_all0_invdistXperiod |  -.0207561   .0209314    -0.99   0.325    -.0624536    .0209414
              hunanXperiod |  -.0606589   .0413385    -1.47   0.146    -.1430094    .0216916
         lnurbanpopXperiod |   .0135036   .0054884     2.46   0.016       .00257    .0244371
            prefcapXperiod |  -.0003768   .0857377    -0.00   0.997    -.1711751    .1704215
           lnjinshiXperiod |  -.0272996   .0226282    -1.21   0.231    -.0723773     .017778
       lncntyquota0Xperiod |  -.0448082    .073172    -0.61   0.542    -.1905743     .100958
          lncntypopXperiod |   .1356481   .0847081     1.60   0.114    -.0330992    .3043953
         lncntyareaXperiod |  -.0777367   .0591591    -1.31   0.193    -.1955877    .0401143
            mainrivXperiod |  -.0709365   .0601526    -1.18   0.242    -.1907665    .0488936
         dist2canalXperiod |   .0795646   .0659484     1.21   0.231    -.0518113    .2109405
             lnriceXperiod |  -.3607363    .212057    -1.70   0.093    -.7831755    .0617028
            lnwheatXperiod |   .1873348    .135699     1.38   0.172    -.0829915    .4576611
       dist_nanjingXperiod |  -.0682249   .0670866    -1.02   0.312    -.2018683    .0654185
     Taiping_route1Xperiod |  -.0106975   .0633566    -0.17   0.866    -.1369103    .1155153
                     _cons |  -.3581142   .4455732    -0.80   0.424    -1.245742    .5295131
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       475         475           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod   Zeng_
> all0_invdistXperiod hunanXperiod )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C5.doc
dir : seeout

. 
. 
. ******************************* Hunan vs. Guangxi, Hubei and Jiangxi
. 
. reghdfe alloff    hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod hunanXperiod lnurbanpopXperiod-Taipin
> g_route1Xperiod   if provcd==11|provcd==5|provcd==16|provcd==10, absorb(year samcntyid ) cluster(prefid samcn
> tyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =     31,122
Absorbing 2 HDFE groups                           F(  15,     51) =      30.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3109
                                                  Adj R-squared   =     0.3009
Number of clusters (prefid)  =         52         Within R-sq.    =     0.0170
Number of clusters (samcntyid) =        342       Root MSE        =     0.4005

                                    (Std. Err. adjusted for 52 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
hXZeng_all0_invdistXperiod |   .0715965   .0308586     2.32   0.024     .0096453    .1335476
  Zeng_all0_invdistXperiod |  -.0142928   .0296544    -0.48   0.632    -.0738265     .045241
              hunanXperiod |   .0398384   .0570947     0.70   0.488    -.0747841    .1544608
         lnurbanpopXperiod |   .0131528   .0083181     1.58   0.120    -.0035466    .0298521
            prefcapXperiod |  -.0154138    .056988    -0.27   0.788     -.129822    .0989943
           lnjinshiXperiod |  -.0132516   .0250615    -0.53   0.599    -.0635648    .0370615
       lncntyquota0Xperiod |  -.0573476   .0635378    -0.90   0.371    -.1849051    .0702099
          lncntypopXperiod |   .0093946   .0707333     0.13   0.895    -.1326085    .1513977
         lncntyareaXperiod |   .0546814   .0466706     1.17   0.247    -.0390138    .1483766
            mainrivXperiod |  -.0567376   .0416316    -1.36   0.179    -.1403165    .0268413
         dist2canalXperiod |   .0794487   .0405593     1.96   0.056    -.0019774    .1608749
             lnriceXperiod |  -.0767006   .0505158    -1.52   0.135    -.1781153    .0247141
            lnwheatXperiod |    .064896   .1075093     0.60   0.549    -.1509378    .2807299
       dist_nanjingXperiod |  -.0776418   .0392027    -1.98   0.053    -.1563445    .0010609
     Taiping_route1Xperiod |  -.0322781   .0720807    -0.45   0.656    -.1769862    .1124299
                     _cons |  -.1906881   .4726953    -0.40   0.688    -1.139663    .7582873
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       342         342           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod   Zeng_
> all0_invdistXperiod hunanXperiod )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C5.doc
dir : seeout

. 
. 
. reghdfe alloff  martyrs_tot_postXperiod    hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod hunanXperiod
>  lnurbanpopXperiod-Taiping_route1Xperiod if provcd==11|provcd==5|provcd==16|provcd==10, absorb(year samcntyid
>  ) cluster(prefid samcntyid)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =     31,122
Absorbing 2 HDFE groups                           F(  16,     51) =      18.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3650
                                                  Adj R-squared   =     0.3558
Number of clusters (prefid)  =         52         Within R-sq.    =     0.0941
Number of clusters (samcntyid) =        342       Root MSE        =     0.3845

                                    (Std. Err. adjusted for 52 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
   martyrs_tot_postXperiod |   .4334417   .0966482     4.48   0.000     .2394123     .627471
hXZeng_all0_invdistXperiod |   .0116402   .0294394     0.40   0.694     -.047462    .0707423
  Zeng_all0_invdistXperiod |  -.0186132    .028738    -0.65   0.520    -.0763071    .0390807
              hunanXperiod |  -.0241519   .0420322    -0.57   0.568    -.1085351    .0602314
         lnurbanpopXperiod |   .0056742    .003437     1.65   0.105    -.0012258    .0125743
            prefcapXperiod |    .018326   .0456114     0.40   0.690    -.0732426    .1098947
           lnjinshiXperiod |  -.0038779   .0263146    -0.15   0.883    -.0567066    .0489509
       lncntyquota0Xperiod |  -.0413665   .0538822    -0.77   0.446    -.1495396    .0668066
          lncntypopXperiod |  -.0517444    .062525    -0.83   0.412    -.1772686    .0737797
         lncntyareaXperiod |   .0854378   .0478874     1.78   0.080    -.0107002    .1815758
            mainrivXperiod |  -.0003734   .0233699    -0.02   0.987    -.0472903    .0465435
         dist2canalXperiod |    .032761   .0462784     0.71   0.482    -.0601469    .1256688
             lnriceXperiod |   -.005202   .0609549    -0.09   0.932     -.127574    .1171701
            lnwheatXperiod |   .0706756   .1134051     0.62   0.536    -.1569947    .2983459
       dist_nanjingXperiod |  -.0360861   .0481588    -0.75   0.457     -.132769    .0605967
     Taiping_route1Xperiod |   .0394014   .0603399     0.65   0.517    -.0817359    .1605388
                     _cons |   .0499284   .3862966     0.13   0.898    -.7255943    .8254512
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       342         342           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod   Zeng_
> all0_invdistXperiod hunanXperiod )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C5.doc
dir : seeout

. 
. 
. 
. ******************************* Hunan vs. Anhui and Jiangsu
. 
. reghdfe alloff  hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod hunanXperiod lnurbanpopXperiod-Taiping_
> route1Xperiod if provcd==11| provcd==3| provcd==2, absorb(year samcntyid) cluster(prefid samcntyid)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =     18,928
Absorbing 2 HDFE groups                           F(  15,     38) =      13.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3961
                                                  Adj R-squared   =     0.3860
Number of clusters (prefid)  =         39         Within R-sq.    =     0.0260
Number of clusters (samcntyid) =        208       Root MSE        =     0.7462

                                    (Std. Err. adjusted for 39 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
hXZeng_all0_invdistXperiod |   .0999109   .0375005     2.66   0.011     .0239951    .1758266
  Zeng_all0_invdistXperiod |  -.0275205   .0333211    -0.83   0.414    -.0949754    .0399345
              hunanXperiod |   .0931223   .2600376     0.36   0.722    -.4332963    .6195409
         lnurbanpopXperiod |   .0370615   .0141045     2.63   0.012     .0085085    .0656145
            prefcapXperiod |  -.1214653   .1546536    -0.79   0.437    -.4345452    .1916146
           lnjinshiXperiod |  -.0555433   .0338949    -1.64   0.110      -.12416    .0130733
       lncntyquota0Xperiod |   .0029629    .141615     0.02   0.983    -.2837216    .2896473
          lncntypopXperiod |   .3718994   .1546911     2.40   0.021     .0587437    .6850551
         lncntyareaXperiod |  -.2584961   .0870389    -2.97   0.005    -.4346972    -.082295
            mainrivXperiod |  -.2108694   .1271653    -1.66   0.106    -.4683021    .0465632
         dist2canalXperiod |    .148702   .0765409     1.94   0.059     -.006247    .3036511
             lnriceXperiod |  -1.104469   .4620563    -2.39   0.022    -2.039853   -.1690851
            lnwheatXperiod |   .7699261   .3567936     2.16   0.037     .0476352    1.492217
       dist_nanjingXperiod |  -.1099825   .0767654    -1.43   0.160     -.265386     .045421
     Taiping_route1Xperiod |  -.1507196   .0769975    -1.96   0.058    -.3065929    .0051538
                     _cons |  -1.381369   1.019158    -1.36   0.183    -3.444547    .6818086
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       208         208           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod   Zeng_
> all0_invdistXperiod hunanXperiod )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C5.doc
dir : seeout

. 
. 
. reghdfe alloff  martyrs_tot_postXperiod hXZeng_all0_invdistXperiod   Zeng_all0_invdistXperiod  hunanXperiod l
> nurbanpopXperiod-Taiping_route1Xperiod   if provcd==11| provcd==3| provcd==2, absorb(year samcntyid) cluster(
> prefid samcntyid)
(MWFE estimator converged in 2 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =     18,928
Absorbing 2 HDFE groups                           F(  16,     38) =      18.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4142
                                                  Adj R-squared   =     0.4043
Number of clusters (prefid)  =         39         Within R-sq.    =     0.0551
Number of clusters (samcntyid) =        208       Root MSE        =     0.7350

                                    (Std. Err. adjusted for 39 clusters in prefid samcntyid)
--------------------------------------------------------------------------------------------
                           |               Robust
                    alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
   martyrs_tot_postXperiod |   .3949729   .1072657     3.68   0.001     .1778248    .6121211
hXZeng_all0_invdistXperiod |   .0421305   .0413938     1.02   0.315     -.041667    .1259279
  Zeng_all0_invdistXperiod |  -.0291448   .0343277    -0.85   0.401    -.0986377     .040348
              hunanXperiod |  -.1899234   .3014613    -0.63   0.532       -.8002    .4203532
         lnurbanpopXperiod |   .0256848   .0123946     2.07   0.045     .0005931    .0507764
            prefcapXperiod |  -.0770922   .1542749    -0.50   0.620    -.3894054    .2352209
           lnjinshiXperiod |  -.0278299   .0407326    -0.68   0.499    -.1102889     .054629
       lncntyquota0Xperiod |  -.0440261    .158568    -0.28   0.783    -.3650303     .276978
          lncntypopXperiod |   .2983914   .1508245     1.98   0.055    -.0069368    .6037195
         lncntyareaXperiod |  -.2198367   .0945025    -2.33   0.025    -.4111469   -.0285265
            mainrivXperiod |  -.1307717   .1287741    -1.02   0.316    -.3914611    .1299178
         dist2canalXperiod |   .1394498   .0814968     1.71   0.095    -.0255318    .3044314
             lnriceXperiod |  -1.056576   .4829912    -2.19   0.035     -2.03434   -.0788112
            lnwheatXperiod |   .7893818   .3681825     2.14   0.038     .0440352    1.534728
       dist_nanjingXperiod |  -.0789747   .0822144    -0.96   0.343    -.2454091    .0874598
     Taiping_route1Xperiod |  -.0666314   .0769975    -0.87   0.392    -.2225047    .0892419
                     _cons |  -.9720643   .9715913    -1.00   0.323    -2.938948    .9948195
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |       208         208           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. outreg2 using Results\Appendix_Table_C5.doc, keep(martyrs_tot_postXperiod  hXZeng_all0_invdistXperiod   Zeng_
> all0_invdistXperiod hunanXperiod )   se  bdec(3) rdec(3) nocons append 
Results\Appendix_Table_C5.doc
dir : seeout

. 
end of do-file

. 
. 
. ******** Table C.6. The Impact of Elite Networks and Elite Power: Controlling for Placebo Networks
. 
. 
. do Programs\Appendix_Table_C6.do

. 
. *********************************************************************************
. *Table C6.The Impact of Elite Networks and Elite Power: Controlling for Placebo Networks
. *********************************************************************************
. 
. 
. use Data\NationalCntyYr.dta,clear

. 
. 
. 
. ********************************************************************************
. ********** gen interactions
. 
. 
. gen hXZeng_exam0_invdist=hunan*Zeng_exam0_invdist

. 
. gen hXinvdist0_F1=invdist0_F1*hunan

. gen hXinvdist0_L1=invdist0_L1*hunan

. 
. 
. 
. foreach x of varlist hunan  Zeng_all0_invdist Zenghu_all_invdist Zeng_exam0_invdist Zeng_Extraexam_invdist hX
> Zeng_exam0_invdist martyrs_tot_post hXinvdist0_F1 hXinvdist0_L1  invdist0_L1  invdist0_F1{
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. foreach x of varlist  lnurbanpop prefcap lnjinshi lncntyquota0 lncntypop lncntyarea  mainriv dist2canal lnric
> e lnwheat dist_nanjing Taiping_route1 {
  2. gen `x'Xperiod=`x'*period
  3. 
. }

. 
. 
. 
. ********************************************************************************
. 
. keep if year > =1820
(32,920 observations deleted)

. 
. ********************************************************************************
. 
. 
. ivreghdfe alloff  (martyrs_tot_postXperiod  = hXZeng_exam0_invdistXperiod )  Zeng_exam0_invdistXperiod hunanX
> period lnurbanpopXperiod-Taiping_route1Xperiod , absorb(year samcntyid ) cluster(prefid )
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on prefid

Number of clusters (prefid) =      255                Number of obs =   149786
                                                      F( 15,   254) =     2.95
                                                      Prob > F      =   0.0002
Total (centered) SS     =  27064.04838                Centered R2   =   0.0151
Total (uncentered) SS   =  27064.04838                Uncentered R2 =   0.0151
Residual SS             =  26654.93926                Root MSE      =     .422

-------------------------------------------------------------------------------------------
                          |               Robust
                   alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
  martyrs_tot_postXperiod |   .6223905   .1616515     3.85   0.000     .3040426    .9407384
Zeng_exam0_invdistXperiod |   .0167941   .0167408     1.00   0.317    -.0161743    .0497626
             hunanXperiod |  -.0864221   .0605132    -1.43   0.154    -.2055936    .0327493
        lnurbanpopXperiod |   .0070486   .0034278     2.06   0.041     .0002981    .0137991
           prefcapXperiod |   .0195937   .0338003     0.58   0.563    -.0469708    .0861583
          lnjinshiXperiod |  -.0203871   .0111682    -1.83   0.069    -.0423811    .0016069
      lncntyquota0Xperiod |  -.0142425   .0222813    -0.64   0.523     -.058122    .0296371
         lncntypopXperiod |   .0194922   .0171842     1.13   0.258    -.0143495    .0533339
        lncntyareaXperiod |  -.0106665   .0151335    -0.70   0.482    -.0404696    .0191366
           mainrivXperiod |  -.0032714    .020044    -0.16   0.870    -.0427449    .0362022
        dist2canalXperiod |   .0039456    .007543     0.52   0.601    -.0109092    .0188005
            lnriceXperiod |  -.0083567   .0442612    -0.19   0.850    -.0955224     .078809
           lnwheatXperiod |  -.0515474    .032421    -1.59   0.113    -.1153956    .0123008
      dist_nanjingXperiod |  -.0039233   .0097964    -0.40   0.689    -.0232157    .0153692
    Taiping_route1Xperiod |  -.0151363   .0599935    -0.25   0.801    -.1332844    .1030118
-------------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              2.930
                                                   Chi-sq(1) P-val =    0.0869
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):             3405.563
                         (Kleibergen-Paap rk Wald F statistic):         12.645
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         martyrs_tot_postXperiod
Included instruments: Zeng_exam0_invdistXperiod hunanXperiod lnurbanpopXperiod
                      prefcapXperiod lnjinshiXperiod lncntyquota0Xperiod
                      lncntypopXperiod lncntyareaXperiod mainrivXperiod
                      dist2canalXperiod lnriceXperiod lnwheatXperiod
                      dist_nanjingXperiod Taiping_route1Xperiod
Excluded instruments: hXZeng_exam0_invdistXperiod
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C6.doc, keep( hXZeng_exam0_invdistXperiod  martyrs_tot_postXperiod   Zen
> g_exam0_invdistXperiod hXinvdist0_L1Xperiod  invdist0_L1Xperiod  hXinvdist0_F1Xperiod  invdist0_F1Xperiod hun
> anXperiod )  se  bdec(3) rdec(3) nocons replace   
Results\Appendix_Table_C6.doc
dir : seeout

. 
. 
. 
. ivreghdfe alloff  (martyrs_tot_postXperiod  = hXZeng_exam0_invdistXperiod )  Zeng_exam0_invdistXperiod hXinvd
> ist0_L1Xperiod  invdist0_L1Xperiod  hXinvdist0_F1Xperiod  invdist0_F1Xperiod  hunanXperiod lnurbanpopXperiod-
> Taiping_route1Xperiod  , absorb(year samcntyid ) cluster(prefid  )
(MWFE estimator converged in 2 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on prefid

Number of clusters (prefid) =      255                Number of obs =   149786
                                                      F( 19,   254) =     9.44
                                                      Prob > F      =   0.0000
Total (centered) SS     =  27064.04838                Centered R2   =   0.0173
Total (uncentered) SS   =  27064.04838                Uncentered R2 =   0.0173
Residual SS             =  26595.96438                Root MSE      =    .4215

-------------------------------------------------------------------------------------------
                          |               Robust
                   alloff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
  martyrs_tot_postXperiod |   .5632641   .1545394     3.64   0.000     .2589222    .8676059
Zeng_exam0_invdistXperiod |   .0141351    .027832     0.51   0.612    -.0406758    .0689459
     hXinvdist0_L1Xperiod |   .0040935    .201629     0.02   0.984    -.3929841    .4011711
       invdist0_L1Xperiod |  -.0456207   .0391497    -1.17   0.245    -.1227201    .0314787
     hXinvdist0_F1Xperiod |  -.0339155    .060985    -0.56   0.579    -.1540161    .0861852
       invdist0_F1Xperiod |   .0702717   .0494187     1.42   0.156     -.027051    .1675943
             hunanXperiod |  -.0597818   .0393777    -1.52   0.130    -.1373302    .0177665
        lnurbanpopXperiod |   .0074326   .0033452     2.22   0.027     .0008448    .0140204
           prefcapXperiod |    .018546    .033281     0.56   0.578    -.0469959    .0840879
          lnjinshiXperiod |  -.0199783   .0109464    -1.83   0.069    -.0415356     .001579
      lncntyquota0Xperiod |  -.0153233   .0222774    -0.69   0.492    -.0591953    .0285486
         lncntypopXperiod |   .0210001    .016952     1.24   0.217    -.0123842    .0543844
        lncntyareaXperiod |  -.0109844   .0149557    -0.73   0.463    -.0404374    .0184686
           mainrivXperiod |  -.0047183   .0196462    -0.24   0.810    -.0434085    .0339719
        dist2canalXperiod |   .0040783   .0074842     0.54   0.586    -.0106606    .0188172
            lnriceXperiod |  -.0083367   .0441041    -0.19   0.850    -.0951929    .0785196
           lnwheatXperiod |  -.0518006   .0325458    -1.59   0.113    -.1158946    .0122934
      dist_nanjingXperiod |  -.0039151   .0097122    -0.40   0.687    -.0230417    .0152115
    Taiping_route1Xperiod |  -.0226496   .0580453    -0.39   0.697    -.1369611    .0916618
-------------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              2.448
                                                   Chi-sq(1) P-val =    0.1177
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):             6295.638
                         (Kleibergen-Paap rk Wald F statistic):         12.899
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         martyrs_tot_postXperiod
Included instruments: Zeng_exam0_invdistXperiod hXinvdist0_L1Xperiod
                      invdist0_L1Xperiod hXinvdist0_F1Xperiod invdist0_F1Xperiod
                      hunanXperiod lnurbanpopXperiod prefcapXperiod
                      lnjinshiXperiod lncntyquota0Xperiod lncntypopXperiod
                      lncntyareaXperiod mainrivXperiod dist2canalXperiod
                      lnriceXperiod lnwheatXperiod dist_nanjingXperiod
                      Taiping_route1Xperiod
Excluded instruments: hXZeng_exam0_invdistXperiod
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        91           0          91     |
   samcntyid |      1646        1646           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
. outreg2 using Results\Appendix_Table_C6.doc, keep( hXZeng_exam0_invdistXperiod  martyrs_tot_postXperiod   Zen
> g_exam0_invdistXperiod hXinvdist0_L1Xperiod  invdist0_L1Xperiod  hXinvdist0_F1Xperiod  invdist0_F1Xperiod hun
> anXperiod )  se  bdec(3) rdec(3) nocons append  
Results\Appendix_Table_C6.doc
dir : seeout

. 
. 
. 
. 
. 
end of do-file

. 
. 
. 
. *********Table C.7. The Changes in Power Distribution by Decade
. 
. 
. do Programs\Appendix_Table_C7.do

. 
. *********************************************************************************************************
. **********  Table C.7. The Changes in Power Distribution by Decade
. *********************************************************************************************************
. 
. 
. use Data\EG_Index.dta, clear

. 
. 
. **** Gen Hunan dummy 
. gen hunan=(provcd==11)

.  
.  
. **** Gen number of offices net of the effect of connections*Hunan 
. gen alloff_NoConn=alloff

. replace alloff_NoConn=alloff_NoConn-estimate*Zeng_all0_invdist if hunan==1
(6,607 real changes made, 3,367 to missing)

. 
. sort provcd year 

. collapse (sum) alloff alloff_NoConn labor, by(provcd year)

. 
. 
. sort year

. by year: egen tot_labor=sum(labor)

. gen x=labor/tot_labor 

.  
. gen x2=x^2

. sort year

. by year: egen sigmax2=sum(x2)

. 
. 
. 
. local vars "alloff  alloff_NoConn"

. 
. foreach y of local vars {
  2. 
. sort year
  3. by year: egen t`y'=sum(`y')
  4. gen s`y'=`y'/t`y'
  5. 
. gen H`y'=1/t`y'
  6. 
. gen sminusx`y'=(s`y'-x)
  7. 
. gen sminusx2`y'=(s`y'-x)^2
  8. sort year
  9. by year: egen G`y'=sum(sminusx2`y')
 10. 
. gen gama`y'=(G`y'-(1-sigmax2)*H`y')/((1-sigmax2)*(1-H`y'))
 11. }

. 
. 
. 
. ****************** ****************** ****************** ****************** 
. 
. keep if provcd==10
(1,547 observations deleted)

. 
. keep year gamaalloff  gamaalloff_NoConn 

. 
. label var gamaalloff "EG Index"

. label var gamaalloff_NoConn "EG Index excluding the Hunan*connected officials"

. 
. ****
. 
. 
. 
. *************************** Table C.7. The Changes in Power Distribution by Decade 
. 
. gen dec=int(year/10)

. table dec

----------------------
      dec |      Freq.
----------+-----------
      182 |         10
      183 |         10
      184 |         10
      185 |         10
      186 |         10
      187 |         10
      188 |         10
      189 |         10
      190 |         10
      191 |          1
----------------------

. table dec, c(mean gamaalloff   mean gamaalloff_NoConn)

------------------------------------------
      dec | mean(gamaal~f)  mean(gamaal~n)
----------+-------------------------------
      182 |       .0096787        .0097987
      183 |       .0110766        .0111667
      184 |       .0139749         .014309
      185 |       .0149146        .0149491
      186 |       .0289491         .014056
      187 |       .0261659        .0206211
      188 |       .0357897        .0243134
      189 |       .0292151        .0255431
      190 |       .0229293        .0225314
      191 |       .0204327        .0204333
------------------------------------------

. 
. 
. 
end of do-file

. 
. 
. ******** Figure 8. The Share of Provincial Officials from Connected Counties in Hunan
. 
. 
. do Programs\Figure_8.do

. 
. *********************************************************************************************************
. **********  Figure 8. The Share of Provincial Officials from Connected Counties in Hunan 
. *********************************************************************************************************
. 
. 
. use Data\ProvGovernors.dta, clear 

. 
. ***** Gen the ratio from Hunan*counnected counties 
. 
. gen RatioWar2053=mobbased2053/num2053

. gen RatioWar5464=mobbased5464/num5464

. gen RatioWar6599=mobbased6599/num6599 

. 
. 
. 
. *************************** Figure 8. The Share of Provincial Officials from Connected Counties in Hunan 
. 
. 
. *Share of top-4 officials from Hunan*connected area
. scatter RatioWar5464 RatioWar2053 if  provinceid!=6,  xlabel(0(0.1)0.3) ylabel(0(0.1)0.3, tposition(inside)) 
> xtitle("From Hunan*connected counties, 1820-53", size(medium)) ytitle("From Hunan*connected counties, 1854-64
> ", size(medium) margin(right)) ms(Oh) mc(blue) mlwidth(medthick) mlabel(prov_en)   mlabcolor(black) mlabposit
> ion(1) xsize(6) ysize(6)  title(A. 1820-53 (Pre-war)  vs. 1854-64 (In-war), size(medium))  graphregion(color(
> white) ifcolor(white) ilcolor(white) fcolor(white)) saving(Results\ProvChief1_HXConn.gph, replace)
(file Results\ProvChief1_HXConn.gph saved)

. 
. 
. 
. **********************************************************
.  
. scatter RatioWar6599  RatioWar5464 if  provinceid!=6 ,  xlabel(0(0.1)0.3) ylabel(0(0.1)0.3, tposition(inside)
> ) xtitle("From Hunan*connected counties, 1854-64", size(medium)) ytitle("From Hunan*connected counties, 1865-
> 99", size(medium) margin(right))   ms(Oh) mcolor(blue) mlwidth(medthick) mlabel(prov_en)  mlabcolor(black) ml
> abposition(1) legend(off) xsize(6) ysize(6)  title(B. 1854-64 (In-war) vs. 1865-99 (Post-war), size(medium)) 
>  graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(white)) saving(Results\ProvChief2_HXConn.gph, 
> replace)
(file Results\ProvChief2_HXConn.gph saved)

. 
. 
. 
. **********************************************************
. 
. twoway lfitci  Yangtze RatioWar5464 if  provinceid!=6  , ciplot(rline) lp(solid) lcolor(black) || scatter Yan
> gtze RatioWar5464 , ms(Oh) mc(blue)  mlwidth(medthick) legend(off) xtitle("From Hunan*connected counties, 185
> 4-64", size(medium) margin(right))   ylabel(0(1)2, tposition(inside))  ytitle("Disobeying the imperial edict"
> , size(medium))  xsize(6) ysize(6)  title(C. Prob. of disobeying the state, size(medium))  graphregion(color(
> white) ifcolor(white) ilcolor(white) fcolor(white)) saving(Results\ProvChief3_HXConn.gph, replace)
(file Results\ProvChief3_HXConn.gph saved)

. 
. 
. 
. graph combine Results\ProvChief1_HXConn.gph Results\ProvChief2_HXConn.gph Results\ProvChief3_HXConn.gph, row(
> 1) xsize(18) ysize(6) graphregion(color(white) ifcolor(white) ilcolor(white) fcolor(white))

. 
. graph export Results\Figure_8.png, replace width(3600) height(1200)
(file Results\Figure_8.png written in PNG format)

. 
end of do-file

. 
. 
. 
. clear 

. log close
      name:  <unnamed>
       log:  D:\Dropbox\Xiangjun\FinalFiles\Replication\Results\All_in_One.log
  log type:  text
 closed on:  13 Oct 2022, 11:13:04
---------------------------------------------------------------------------------------------------------------
